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https://paperswithcode.com/paper/2d-instance-editing-in-3d-space
|
2507.05819
| null | null |
2D Instance Editing in 3D Space
|
Generative models have achieved significant progress in advancing 2D image editing, demonstrating exceptional precision and realism. However, they often struggle with consistency and object identity preservation due to their inherent pixel-manipulation nature. To address this limitation, we introduce a novel "2D-3D-2D" framework. Our approach begins by lifting 2D objects into 3D representation, enabling edits within a physically plausible, rigidity-constrained 3D environment. The edited 3D objects are then reprojected and seamlessly inpainted back into the original 2D image. In contrast to existing 2D editing methods, such as DragGAN and DragDiffusion, our method directly manipulates objects in a 3D environment. Extensive experiments highlight that our framework surpasses previous methods in general performance, delivering highly consistent edits while robustly preserving object identity.
| null |
https://arxiv.org/abs/2507.05819v1
|
https://arxiv.org/pdf/2507.05819v1.pdf
| null |
[
"Yuhuan Xie",
"Aoxuan Pan",
"Ming-Xian Lin",
"Wei Huang",
"Yi-Hua Huang",
"Xiaojuan Qi"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/just-say-better-or-worse-a-human-ai
|
2507.05815
| null | null |
Just Say Better or Worse: A Human-AI Collaborative Framework for Medical Image Segmentation Without Manual Annotations
|
Manual annotation of medical images is a labor-intensive and time-consuming process, posing a significant bottleneck in the development and deployment of robust medical imaging AI systems. This paper introduces a novel Human-AI collaborative framework for medical image segmentation that substantially reduces the annotation burden by eliminating the need for explicit manual pixel-level labeling. The core innovation lies in a preference learning paradigm, where human experts provide minimal, intuitive feedback -- simply indicating whether an AI-generated segmentation is better or worse than a previous version. The framework comprises four key components: (1) an adaptable foundation model (FM) for feature extraction, (2) label propagation based on feature similarity, (3) a clicking agent that learns from human better-or-worse feedback to decide where to click and with which label, and (4) a multi-round segmentation learning procedure that trains a state-of-the-art segmentation network using pseudo-labels generated by the clicking agent and FM-based label propagation. Experiments on three public datasets demonstrate that the proposed approach achieves competitive segmentation performance using only binary preference feedback, without requiring experts to directly manually annotate the images.
| null |
https://arxiv.org/abs/2507.05815v1
|
https://arxiv.org/pdf/2507.05815v1.pdf
| null |
[
"Yizhe Zhang"
] |
[
"Image Segmentation",
"Medical Image Segmentation",
"Segmentation",
"Semantic Segmentation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/empowering-bridge-digital-twins-by-bridging
|
2507.05814
| null | null |
Empowering Bridge Digital Twins by Bridging the Data Gap with a Unified Synthesis Framework
|
As critical transportation infrastructure, bridges face escalating challenges from aging and deterioration, while traditional manual inspection methods suffer from low efficiency. Although 3D point cloud technology provides a new data-driven paradigm, its application potential is often constrained by the incompleteness of real-world data, which results from missing labels and scanning occlusions. To overcome the bottleneck of insufficient generalization in existing synthetic data methods, this paper proposes a systematic framework for generating 3D bridge data. This framework can automatically generate complete point clouds featuring component-level instance annotations, high-fidelity color, and precise normal vectors. It can be further extended to simulate the creation of diverse and physically realistic incomplete point clouds, designed to support the training of segmentation and completion networks, respectively. Experiments demonstrate that a PointNet++ model trained with our synthetic data achieves a mean Intersection over Union (mIoU) of 84.2% in real-world bridge semantic segmentation. Concurrently, a fine-tuned KT-Net exhibits superior performance on the component completion task. This research offers an innovative methodology and a foundational dataset for the 3D visual analysis of bridge structures, holding significant implications for advancing the automated management and maintenance of infrastructure.
| null |
https://arxiv.org/abs/2507.05814v2
|
https://arxiv.org/pdf/2507.05814v2.pdf
| null |
[
"Wang Wang",
"Mingyu Shi",
"Jun Jiang",
"Wenqian Ma",
"Chong Liu",
"Yasutaka Narazaki",
"Xuguang Wang"
] |
[
"Missing Labels",
"Semantic Segmentation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/spade-spatial-aware-denoising-network-for
|
2507.05798
| null | null |
SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning
|
Panoptic Scene Graph Generation (PSG) integrates instance segmentation with relation understanding to capture pixel-level structural relationships in complex scenes. Although recent approaches leveraging pre-trained vision-language models (VLMs) have significantly improved performance in the open-vocabulary setting, they commonly ignore the inherent limitations of VLMs in spatial relation reasoning, such as difficulty in distinguishing object relative positions, which results in suboptimal relation prediction. Motivated by the denoising diffusion model's inversion process in preserving the spatial structure of input images, we propose SPADE (SPatial-Aware Denoising-nEtwork) framework -- a novel approach for open-vocabulary PSG. SPADE consists of two key steps: (1) inversion-guided calibration for the UNet adaptation, and (2) spatial-aware context reasoning. In the first step, we calibrate a general pre-trained teacher diffusion model into a PSG-specific denoising network with cross-attention maps derived during inversion through a lightweight LoRA-based fine-tuning strategy. In the second step, we develop a spatial-aware relation graph transformer that captures both local and long-range contextual information, facilitating the generation of high-quality relation queries. Extensive experiments on benchmark PSG and Visual Genome datasets demonstrate that SPADE outperforms state-of-the-art methods in both closed- and open-set scenarios, particularly for spatial relationship prediction.
| null |
https://arxiv.org/abs/2507.05798v1
|
https://arxiv.org/pdf/2507.05798v1.pdf
| null |
[
"Xin Hu",
"Ke Qin",
"Guiduo Duan",
"Ming Li",
"Yuan-Fang Li",
"Tao He"
] |
[
"Denoising",
"Graph Generation",
"Instance Segmentation",
"Panoptic Scene Graph Generation",
"Relation",
"Relation Prediction",
"Scene Graph Generation",
"Semantic Segmentation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
},
{
"code_snippet_url": "",
"description": "**SPADE**, or **Spatially-Adaptive Normalization** is a conditional normalization method for semantic image synthesis. Similar to [Batch Normalization](https://www.paperswithcode.com/method/batch-normalization), the activation is normalized in the channel-wise manner and then modulated with learned scale and bias. In the SPADE, the mask is first projected onto an embedding space and then convolved to produce the modulation parameters $\\gamma$ and $\\beta .$ Unlike prior conditional normalization methods, $\\gamma$ and $\\mathbf{\\beta}$ are not vectors, but tensors with spatial dimensions. The produced $\\gamma$ and $\\mathbf{\\beta}$ are multiplied and added to the normalized activation element-wise.",
"full_name": "Spatially-Adaptive Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "SPADE",
"source_title": "Semantic Image Synthesis with Spatially-Adaptive Normalization",
"source_url": "https://arxiv.org/abs/1903.07291v2"
}
] |
https://paperswithcode.com/paper/psat-pediatric-segmentation-approaches-via
|
2507.05764
| null | null |
PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning
|
Pediatric medical imaging presents unique challenges due to significant anatomical and developmental differences compared to adults. Direct application of segmentation models trained on adult data often yields suboptimal performance, particularly for small or rapidly evolving structures. To address these challenges, several strategies leveraging the nnU-Net framework have been proposed, differing along four key axes: (i) the fingerprint dataset (adult, pediatric, or a combination thereof) from which the Training Plan -including the network architecture-is derived; (ii) the Learning Set (adult, pediatric, or mixed), (iii) Data Augmentation parameters, and (iv) the Transfer learning method (finetuning versus continual learning). In this work, we introduce PSAT (Pediatric Segmentation Approaches via Adult Augmentations and Transfer learning), a systematic study that investigates the impact of these axes on segmentation performance. We benchmark the derived strategies on two pediatric CT datasets and compare them with state-of-theart methods, including a commercial radiotherapy solution. PSAT highlights key pitfalls and provides actionable insights for improving pediatric segmentation. Our experiments reveal that a training plan based on an adult fingerprint dataset is misaligned with pediatric anatomy-resulting in significant performance degradation, especially when segmenting fine structures-and that continual learning strategies mitigate institutional shifts, thus enhancing generalization across diverse pediatric datasets. The code is available at https://github.com/ICANS-Strasbourg/PSAT.
|
Pediatric medical imaging presents unique challenges due to significant anatomical and developmental differences compared to adults.
|
https://arxiv.org/abs/2507.05764v1
|
https://arxiv.org/pdf/2507.05764v1.pdf
| null |
[
"Tristan Kirscher",
"Sylvain Faisan",
"Xavier Coubez",
"Loris Barrier",
"Philippe Meyer"
] |
[
"Anatomy",
"Continual Learning",
"Data Augmentation",
"Segmentation",
"Transfer Learning"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/dreamart-generating-interactable-articulated
|
2507.05763
| null | null |
DreamArt: Generating Interactable Articulated Objects from a Single Image
|
Generating articulated objects, such as laptops and microwaves, is a crucial yet challenging task with extensive applications in Embodied AI and AR/VR. Current image-to-3D methods primarily focus on surface geometry and texture, neglecting part decomposition and articulation modeling. Meanwhile, neural reconstruction approaches (e.g., NeRF or Gaussian Splatting) rely on dense multi-view or interaction data, limiting their scalability. In this paper, we introduce DreamArt, a novel framework for generating high-fidelity, interactable articulated assets from single-view images. DreamArt employs a three-stage pipeline: firstly, it reconstructs part-segmented and complete 3D object meshes through a combination of image-to-3D generation, mask-prompted 3D segmentation, and part amodal completion. Second, we fine-tune a video diffusion model to capture part-level articulation priors, leveraging movable part masks as prompt and amodal images to mitigate ambiguities caused by occlusion. Finally, DreamArt optimizes the articulation motion, represented by a dual quaternion, and conducts global texture refinement and repainting to ensure coherent, high-quality textures across all parts. Experimental results demonstrate that DreamArt effectively generates high-quality articulated objects, possessing accurate part shape, high appearance fidelity, and plausible articulation, thereby providing a scalable solution for articulated asset generation. Our project page is available at https://dream-art-0.github.io/DreamArt/.
| null |
https://arxiv.org/abs/2507.05763v1
|
https://arxiv.org/pdf/2507.05763v1.pdf
| null |
[
"Ruijie Lu",
"Yu Liu",
"Jiaxiang Tang",
"Junfeng Ni",
"Yuxiang Wang",
"Diwen Wan",
"Gang Zeng",
"Yixin Chen",
"Siyuan Huang"
] |
[
"3D Generation",
"Image to 3D",
"NeRF"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/spatial-and-semantic-embedding-integration
|
2507.04845
| null | null |
Spatial and Semantic Embedding Integration for Stereo Sound Event Localization and Detection in Regular Videos
|
This report presents our systems submitted to the audio-only and audio-visual tracks of the DCASE2025 Task 3 Challenge: Stereo Sound Event Localization and Detection (SELD) in Regular Video Content. SELD is a complex task that combines temporal event classification with spatial localization, requiring reasoning across spatial, temporal, and semantic dimensions. The last is arguably the most challenging to model. Traditional SELD architectures rely on multichannel input, which limits their ability to leverage large-scale pre-training due to data constraints. To address this, we enhance standard SELD architectures with semantic information by integrating pre-trained, contrastive language-aligned models: CLAP for audio and OWL-ViT for visual inputs. These embeddings are incorporated into a modified Conformer module tailored for multimodal fusion, which we refer to as the Cross-Modal Conformer. Additionally, we incorporate autocorrelation-based acoustic features to improve distance estimation. We pre-train our models on curated synthetic audio and audio-visual datasets and apply a left-right channel swapping augmentation to further increase the training data. Both our audio-only and audio-visual systems substantially outperform the challenge baselines on the development set, demonstrating the effectiveness of our strategy. Performance is further improved through model ensembling and a visual post-processing step based on human keypoints. Future work will investigate the contribution of each modality and explore architectural variants to further enhance results.
| null |
https://arxiv.org/abs/2507.04845v1
|
https://arxiv.org/pdf/2507.04845v1.pdf
| null |
[
"Davide Berghi",
"Philip J. B. Jackson"
] |
[
"Sound Event Localization and Detection"
] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ripe-reinforcement-learning-on-unlabeled
|
2507.04839
| null | null |
RIPE: Reinforcement Learning on Unlabeled Image Pairs for Robust Keypoint Extraction
|
We introduce RIPE, an innovative reinforcement learning-based framework for weakly-supervised training of a keypoint extractor that excels in both detection and description tasks. In contrast to conventional training regimes that depend heavily on artificial transformations, pre-generated models, or 3D data, RIPE requires only a binary label indicating whether paired images represent the same scene. This minimal supervision significantly expands the pool of training data, enabling the creation of a highly generalized and robust keypoint extractor. RIPE utilizes the encoder's intermediate layers for the description of the keypoints with a hyper-column approach to integrate information from different scales. Additionally, we propose an auxiliary loss to enhance the discriminative capability of the learned descriptors. Comprehensive evaluations on standard benchmarks demonstrate that RIPE simplifies data preparation while achieving competitive performance compared to state-of-the-art techniques, marking a significant advancement in robust keypoint extraction and description. To support further research, we have made our code publicly available at https://github.com/fraunhoferhhi/RIPE.
|
We introduce RIPE, an innovative reinforcement learning-based framework for weakly-supervised training of a keypoint extractor that excels in both detection and description tasks.
|
https://arxiv.org/abs/2507.04839v1
|
https://arxiv.org/pdf/2507.04839v1.pdf
| null |
[
"Johannes Künzel",
"Anna Hilsmann",
"Peter Eisert"
] |
[] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/moremouse-monocular-reconstruction-of
|
2507.04258
| null | null |
MoReMouse: Monocular Reconstruction of Laboratory Mouse
|
Laboratory mice play a crucial role in biomedical research, yet accurate 3D mouse surface motion reconstruction remains challenging due to their complex non-rigid geometric deformations and textureless appearance. Moreover, the absence of structured 3D datasets severely hinders the progress beyond sparse keypoint tracking. To narrow the gap, we present MoReMouse, the first monocular dense 3D reconstruction network tailored for laboratory mice. To achieve this goal, we highlight three key designs. First, we construct the first high-fidelity dense-view synthetic dataset for mice, by rendering our self-designed realistic Gaussian mouse avatar. Second, MoReMouse adopts a transformer-based feedforward architecture with triplane representation, achieving high-quality 3D surface generation from a single image. Third, we create geodesic-based continuous correspondence embeddings on mouse surface, which serve as strong semantic priors to improve reconstruction stability and surface consistency. Extensive quantitative and qualitative experiments demonstrate that MoReMouse significantly outperforms existing open-source methods in accuracy and robustness. Video results are available at https://zyyw-eric.github.io/MoreMouse-webpage/.
| null |
https://arxiv.org/abs/2507.04258v1
|
https://arxiv.org/pdf/2507.04258v1.pdf
| null |
[
"Yuan Zhong",
"Jingxiang Sun",
"Liang An",
"Yebin Liu"
] |
[
"3D Reconstruction",
"3D Surface Generation",
"Monocular Reconstruction"
] | 2025-07-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/grid-reg-grid-based-sar-and-optical-image
|
2507.04233
| null | null |
Grid-Reg: Grid-Based SAR and Optical Image Registration Across Platforms
|
Registering airborne SAR with spaceborne optical images is crucial for SAR image interpretation and geo-localization. It is challenging for this cross-platform heterogeneous image registration due to significant geometric and radiation differences, which current methods fail to handle. To tackle these challenges, we propose a novel grid-based multimodal registration framework (Grid-Reg) across airborne and space-born platforms, including a new domain-robust descriptor extraction network, Hybrid Siamese Correlation Metric Learning Network (HSCMLNet) and a grid-based solver (Grid-solver) for transformation parameters estimation. Our Grid-Reg is based on detector-free and global matching loss rather than accurate keypoint correspondences. These accurate correspondences are inherently difficult in heterogeneous images with large geometric deformation. By Grid-Solver, our Grid-Reg estimates transformation parameters by optimizing robust global matching loss-based patch correspondences of whole images in a coarse-to-fine strategy. To robustly calculate the similarity between patches, specifically that have noise and change objects, we propose HSCMLNet, including a hybrid Siamese module to extract high-level features of multimodal images and a correlation learning module (CMLModule) based equiangular unit basis vectors (EUBVs). Moreover, we propose a manifold loss EUBVsLoss to constrain the normalized correlation between local embeddings of patches and EUBVs. Furthermore, we curate a new challenging benchmark dataset of SAR-to-optical registration using real-world UAV MiniSAR data and optical images from Google Earth. We extensively analyze factors affecting registration accuracy and compare our method with state-of-the-art techniques on this dataset, showing superior performance.
| null |
https://arxiv.org/abs/2507.04233v1
|
https://arxiv.org/pdf/2507.04233v1.pdf
| null |
[
"Xiaochen Wei",
"Weiwei Guo",
"Zenghui Zhang",
"Wenxian Yu"
] |
[
"geo-localization",
"Image Registration",
"Metric Learning"
] | 2025-07-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/combining-graph-neural-networks-and-mixed
|
2507.03920
| null | null |
Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model
|
Recently, a novel two-phase framework named mol-infer for inference of chemical compounds with prescribed abstract structures and desired property values has been proposed. The framework mol-infer is primarily based on using mixed integer linear programming (MILP) to simulate the computational process of machine learning methods and describe the necessary and sufficient conditions to ensure such a chemical graph exists. The existing approaches usually first convert the chemical compounds into handcrafted feature vectors to construct prediction functions, but because of the limit on the kinds of descriptors originated from the need for tractability in the MILP formulation, the learning performances on datasets of some properties are not good enough. A lack of good learning performance can greatly lower the quality of the inferred chemical graphs, and thus improving learning performance is of great importance. On the other hand, graph neural networks (GNN) offer a promising machine learning method to directly utilize the chemical graphs as the input, and many existing GNN-based approaches to the molecular property prediction problem have shown that they can enjoy better learning performances compared to the traditional approaches that are based on feature vectors. In this study, we develop a molecular inference framework based on mol-infer, namely mol-infer-GNN, that utilizes GNN as the learning method while keeping the great flexibility originated from the two-layered model on the abstract structure of the chemical graph to be inferred. We conducted computational experiments on the QM9 dataset to show that our proposed GNN model can obtain satisfying learning performances for some properties despite its simple structure, and can infer small chemical graphs comprising up to 20 non-hydrogen atoms within reasonable computational time.
| null |
https://arxiv.org/abs/2507.03920v1
|
https://arxiv.org/pdf/2507.03920v1.pdf
| null |
[
"Jianshen Zhu",
"Naveed Ahmed Azam",
"Kazuya Haraguchi",
"Liang Zhao",
"Tatsuya Akutsu"
] |
[
"Molecular Property Prediction",
"Property Prediction"
] | 2025-07-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/beyond-accuracy-metrics-that-uncover-what
|
2507.03542
| null | null |
Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual Descriptor
|
Text-based visual descriptors--ranging from simple class names to more descriptive phrases--are widely used in visual concept discovery and image classification with vision-language models (VLMs). Their effectiveness, however, depends on a complex interplay of factors, including semantic clarity, presence in the VLM's pre-training data, and how well the descriptors serve as a meaningful representation space. In this work, we systematically analyze descriptor quality along two key dimensions: (1) representational capacity, and (2) relationship with VLM pre-training data. We evaluate a spectrum of descriptor generation methods, from zero-shot LLM-generated prompts to iteratively refined descriptors. Motivated by ideas from representation alignment and language understanding, we introduce two alignment-based metrics--Global Alignment and CLIP Similarity--that move beyond accuracy. These metrics shed light on how different descriptor generation strategies interact with foundation model properties, offering new ways to study descriptor effectiveness beyond accuracy evaluations.
|
Text-based visual descriptors--ranging from simple class names to more descriptive phrases--are widely used in visual concept discovery and image classification with vision-language models (VLMs).
|
https://arxiv.org/abs/2507.03542v2
|
https://arxiv.org/pdf/2507.03542v2.pdf
| null |
[
"Ethan Lin",
"Linxi Zhao",
"Atharva Sehgal",
"Jennifer J. Sun"
] |
[
"Descriptive",
"image-classification",
"Image Classification"
] | 2025-07-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/OpenAI/CLIP",
"description": "**Contrastive Language-Image Pre-training** (**CLIP**), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. \r\n\r\nFor pre-training, CLIP is trained to predict which of the $N X N$ possible (image, text) pairings across a batch actually occurred. CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximize the cosine similarity of the image and text embeddings of the $N$ real pairs in the batch while minimizing the cosine similarity of the embeddings of the $N^2 - N$ incorrect pairings. A symmetric cross entropy loss is optimized over these similarity scores. \r\n\r\nImage credit: [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/pdf/2103.00020.pdf)",
"full_name": "Contrastive Language-Image Pre-training",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Representations",
"parent": null
},
"name": "CLIP",
"source_title": "Learning Transferable Visual Models From Natural Language Supervision",
"source_url": "https://arxiv.org/abs/2103.00020v1"
}
] |
https://paperswithcode.com/paper/molecular-machine-learning-using-euler
|
2507.03474
| null | null |
Molecular Machine Learning Using Euler Characteristic Transforms
|
The shape of a molecule determines its physicochemical and biological properties. However, it is often underrepresented in standard molecular representation learning approaches. Here, we propose using the Euler Characteristic Transform (ECT) as a geometrical-topological descriptor. Computed directly on a molecular graph derived from handcrafted atomic features, the ECT enables the extraction of multiscale structural features, offering a novel way to represent and encode molecular shape in the feature space. We assess the predictive performance of this representation across nine benchmark regression datasets, all centered around predicting the inhibition constant $K_i$. In addition, we compare our proposed ECT-based representation against traditional molecular representations and methods, such as molecular fingerprints/descriptors and graph neural networks (GNNs). Our results show that our ECT-based representation achieves competitive performance, ranking among the best-performing methods on several datasets. More importantly, its combination with traditional representations, particularly with the AVALON fingerprint, significantly \emph{enhances predictive performance}, outperforming other methods on most datasets. These findings highlight the complementary value of multiscale topological information and its potential for being combined with established techniques. Our study suggests that hybrid approaches incorporating explicit shape information can lead to more informative and robust molecular representations, enhancing and opening new avenues in molecular machine learning tasks. To support reproducibility and foster open biomedical research, we provide open access to all experiments and code used in this work.
|
More importantly, its combination with traditional representations, particularly with the AVALON fingerprint, significantly \emph{enhances predictive performance}, outperforming other methods on most datasets.
|
https://arxiv.org/abs/2507.03474v1
|
https://arxiv.org/pdf/2507.03474v1.pdf
| null |
[
"Victor Toscano-Duran",
"Florian Rottach",
"Bastian Rieck"
] |
[
"molecular representation",
"Representation Learning"
] | 2025-07-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/helping-clip-see-both-the-forest-and-the
|
2507.03458
| null | null |
Helping CLIP See Both the Forest and the Trees: A Decomposition and Description Approach
|
Vision-Language Models (VLMs) like CLIP achieve cross-modal semantic alignment through contrastive learning, exhibiting robust zero-shot generalization. Traditional prompt engineering, however, predominantly relies on coarse-grained category labels, neglecting fine-grained local semantics. Existing approaches assume that VLMs inherently recognize localized visual details and attempt to enhance classification by augmenting text prompts with attribute descriptors generated by large language models. However, our systematic experiments reveal critical limitations: CLIP's strong bias toward global image patterns hinders its ability to process localized visual descriptors. To address this fundamental constraint, we propose a simple, effective, and plug-and-play solution that enables CLIP to ``See Both the Forest and the Trees." Specifically, we employ stochastic multi-crop augmentation to activate CLIP's latent capacity for localized feature analysis. By cropping only partial regions, the approach effectively constrains the model's receptive field and recalibrates its attention mechanism, thereby mitigating its inherent bias. We evaluate the proposed method under zero-shot, few-shot, and test-time adaptation settings, and extensive experiments demonstrate that D&D achieves promising performance.
| null |
https://arxiv.org/abs/2507.03458v1
|
https://arxiv.org/pdf/2507.03458v1.pdf
| null |
[
"Leyan Xue",
"Zongbo Han",
"Guangyu Wang",
"QinGhua Hu",
"Mingyue Cheng",
"Changqing Zhang"
] |
[
"Attribute",
"Contrastive Learning",
"Prompt Engineering",
"Test-time Adaptation",
"Zero-shot Generalization"
] | 2025-07-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/OpenAI/CLIP",
"description": "**Contrastive Language-Image Pre-training** (**CLIP**), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. \r\n\r\nFor pre-training, CLIP is trained to predict which of the $N X N$ possible (image, text) pairings across a batch actually occurred. CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximize the cosine similarity of the image and text embeddings of the $N$ real pairs in the batch while minimizing the cosine similarity of the embeddings of the $N^2 - N$ incorrect pairings. A symmetric cross entropy loss is optimized over these similarity scores. \r\n\r\nImage credit: [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/pdf/2103.00020.pdf)",
"full_name": "Contrastive Language-Image Pre-training",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Representations",
"parent": null
},
"name": "CLIP",
"source_title": "Learning Transferable Visual Models From Natural Language Supervision",
"source_url": "https://arxiv.org/abs/2103.00020v1"
}
] |
https://paperswithcode.com/paper/pose-star-anatomy-aware-editing-for-open
|
2507.03402
| null | null |
Pose-Star: Anatomy-Aware Editing for Open-World Fashion Images
|
To advance real-world fashion image editing, we analyze existing two-stage pipelines(mask generation followed by diffusion-based editing)which overly prioritize generator optimization while neglecting mask controllability. This results in two critical limitations: I) poor user-defined flexibility (coarse-grained human masks restrict edits to predefined regions like upper torso; fine-grained clothes masks preserve poses but forbid style/length customization). II) weak pose robustness (mask generators fail due to articulated poses and miss rare regions like waist, while human parsers remain limited by predefined categories). To address these gaps, we propose Pose-Star, a framework that dynamically recomposes body structures (e.g., neck, chest, etc.) into anatomy-aware masks (e.g., chest-length) for user-defined edits. In Pose-Star, we calibrate diffusion-derived attention (Star tokens) via skeletal keypoints to enhance rare structure localization in complex poses, suppress noise through phase-aware analysis of attention dynamics (Convergence,Stabilization,Divergence) with threshold masking and sliding-window fusion, and refine edges via cross-self attention merging and Canny alignment. This work bridges controlled benchmarks and open-world demands, pioneering anatomy-aware, pose-robust editing and laying the foundation for industrial fashion image editing.
| null |
https://arxiv.org/abs/2507.03402v1
|
https://arxiv.org/pdf/2507.03402v1.pdf
| null |
[
"Yuran Dong",
"Mang Ye"
] |
[
"Anatomy"
] | 2025-07-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/lantern-a-machine-learning-framework-for
|
2507.03209
| null | null |
LANTERN: A Machine Learning Framework for Lipid Nanoparticle Transfection Efficiency Prediction
|
The discovery of new ionizable lipids for efficient lipid nanoparticle (LNP)-mediated RNA delivery remains a critical bottleneck for RNA-based therapeutics development. Recent advances have highlighted the potential of machine learning (ML) to predict transfection efficiency from molecular structure, enabling high-throughput virtual screening and accelerating lead identification. However, existing approaches are hindered by inadequate data quality, ineffective feature representations, low predictive accuracy, and poor generalizability. Here, we present LANTERN (Lipid nANoparticle Transfection Efficiency pRedictioN), a robust ML framework for predicting transfection efficiency based on ionizable lipid representation. We benchmarked a diverse set of ML models against AGILE, a previously published model developed for transfection prediction. Our results show that combining simpler models with chemically informative features, particularly count-based Morgan fingerprints, outperforms more complex models that rely on internally learned embeddings, such as AGILE. We also show that a multi-layer perceptron trained on a combination of Morgan fingerprints and Expert descriptors achieved the highest performance ($\text{R}^2$ = 0.8161, r = 0.9053), significantly exceeding AGILE ($\text{R}^2$ = 0.2655, r = 0.5488). We show that the models in LANTERN consistently have strong performance across multiple evaluation metrics. Thus, LANTERN offers a robust benchmarking framework for LNP transfection prediction and serves as a valuable tool for accelerating lipid-based RNA delivery systems design.
|
Here, we present LANTERN (Lipid nANoparticle Transfection Efficiency pRedictioN), a robust ML framework for predicting transfection efficiency based on ionizable lipid representation.
|
https://arxiv.org/abs/2507.03209v1
|
https://arxiv.org/pdf/2507.03209v1.pdf
| null |
[
"Asal Mehradfar",
"Mohammad Shahab Sepehri",
"Jose Miguel Hernandez-Lobato",
"Glen S. Kwon",
"Mahdi Soltanolkotabi",
"Salman Avestimehr",
"Morteza Rasoulianboroujeni"
] |
[
"Benchmarking"
] | 2025-07-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/unimc-taming-diffusion-transformer-for
|
2507.02713
| null | null |
UniMC: Taming Diffusion Transformer for Unified Keypoint-Guided Multi-Class Image Generation
|
Although significant advancements have been achieved in the progress of keypoint-guided Text-to-Image diffusion models, existing mainstream keypoint-guided models encounter challenges in controlling the generation of more general non-rigid objects beyond humans (e.g., animals). Moreover, it is difficult to generate multiple overlapping humans and animals based on keypoint controls solely. These challenges arise from two main aspects: the inherent limitations of existing controllable methods and the lack of suitable datasets. First, we design a DiT-based framework, named UniMC, to explore unifying controllable multi-class image generation. UniMC integrates instance- and keypoint-level conditions into compact tokens, incorporating attributes such as class, bounding box, and keypoint coordinates. This approach overcomes the limitations of previous methods that struggled to distinguish instances and classes due to their reliance on skeleton images as conditions. Second, we propose HAIG-2.9M, a large-scale, high-quality, and diverse dataset designed for keypoint-guided human and animal image generation. HAIG-2.9M includes 786K images with 2.9M instances. This dataset features extensive annotations such as keypoints, bounding boxes, and fine-grained captions for both humans and animals, along with rigorous manual inspection to ensure annotation accuracy. Extensive experiments demonstrate the high quality of HAIG-2.9M and the effectiveness of UniMC, particularly in heavy occlusions and multi-class scenarios.
| null |
https://arxiv.org/abs/2507.02713v2
|
https://arxiv.org/pdf/2507.02713v2.pdf
| null |
[
"Qin Guo",
"Ailing Zeng",
"Dongxu Yue",
"Ceyuan Yang",
"Yang Cao",
"Hanzhong Guo",
"Fei Shen",
"Wei Liu",
"Xihui Liu",
"Dan Xu"
] |
[
"Image Generation"
] | 2025-07-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/reconstructing-close-human-interaction-with-1
|
2507.02565
| null | null |
Reconstructing Close Human Interaction with Appearance and Proxemics Reasoning
|
Due to visual ambiguities and inter-person occlusions, existing human pose estimation methods cannot recover plausible close interactions from in-the-wild videos. Even state-of-the-art large foundation models~(\eg, SAM) cannot accurately distinguish human semantics in such challenging scenarios. In this work, we find that human appearance can provide a straightforward cue to address these obstacles. Based on this observation, we propose a dual-branch optimization framework to reconstruct accurate interactive motions with plausible body contacts constrained by human appearances, social proxemics, and physical laws. Specifically, we first train a diffusion model to learn the human proxemic behavior and pose prior knowledge. The trained network and two optimizable tensors are then incorporated into a dual-branch optimization framework to reconstruct human motions and appearances. Several constraints based on 3D Gaussians, 2D keypoints, and mesh penetrations are also designed to assist the optimization. With the proxemics prior and diverse constraints, our method is capable of estimating accurate interactions from in-the-wild videos captured in complex environments. We further build a dataset with pseudo ground-truth interaction annotations, which may promote future research on pose estimation and human behavior understanding. Experimental results on several benchmarks demonstrate that our method outperforms existing approaches. The code and data are available at https://www.buzhenhuang.com/works/CloseApp.html.
| null |
https://arxiv.org/abs/2507.02565v1
|
https://arxiv.org/pdf/2507.02565v1.pdf
|
CVPR 2025 1
|
[
"Buzhen Huang",
"Chen Li",
"Chongyang Xu",
"Dongyue Lu",
"Jinnan Chen",
"Yangang Wang",
"Gim Hee Lee"
] |
[
"Pose Estimation"
] | 2025-07-03T00:00:00 |
http://openaccess.thecvf.com//content/CVPR2025/html/Huang_Reconstructing_Close_Human_Interaction_with_Appearance_and_Proxemics_Reasoning_CVPR_2025_paper.html
|
http://openaccess.thecvf.com//content/CVPR2025/papers/Huang_Reconstructing_Close_Human_Interaction_with_Appearance_and_Proxemics_Reasoning_CVPR_2025_paper.pdf
|
reconstructing-close-human-interaction-with
| null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/lmpnet-for-weakly-supervised-keypoint
|
2507.02308
| null | null |
LMPNet for Weakly-supervised Keypoint Discovery
|
In this work, we explore the task of semantic object keypoint discovery weakly-supervised by only category labels. This is achieved by transforming discriminatively-trained intermediate layer filters into keypoint detectors. We begin by identifying three preferred characteristics of keypoint detectors: (i) spatially sparse activations, (ii) consistency and (iii) diversity. Instead of relying on hand-crafted loss terms, a novel computationally-efficient leaky max pooling (LMP) layer is proposed to explicitly encourage final conv-layer filters to learn "non-repeatable local patterns" that are well aligned with object keypoints. Informed by visualizations, a simple yet effective selection strategy is proposed to ensure consistent filter activations and attention mask-out is then applied to force the network to distribute its attention to the whole object instead of just the most discriminative region. For the final keypoint prediction, a learnable clustering layer is proposed to group keypoint proposals into keypoint predictions. The final model, named LMPNet, is highly interpretable in that it directly manipulates network filters to detect predefined concepts. Our experiments show that LMPNet can (i) automatically discover semantic keypoints that are robust to object pose and (ii) achieves strong prediction accuracy comparable to a supervised pose estimation model.
| null |
https://arxiv.org/abs/2507.02308v1
|
https://arxiv.org/pdf/2507.02308v1.pdf
| null |
[
"Pei Guo",
"Ryan Farrell"
] |
[
"Object",
"Pose Estimation"
] | 2025-07-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/gdc-cohort-copilot-an-ai-copilot-for-curating
|
2507.02221
| null | null |
GDC Cohort Copilot: An AI Copilot for Curating Cohorts from the Genomic Data Commons
|
Motivation: The Genomic Data Commons (GDC) provides access to high quality, harmonized cancer genomics data through a unified curation and analysis platform centered around patient cohorts. While GDC users can interactively create complex cohorts through the graphical Cohort Builder, users (especially new ones) may struggle to find specific cohort descriptors across hundreds of possible fields and properties. However, users may be better able to describe their desired cohort in free-text natural language. Results: We introduce GDC Cohort Copilot, an open-source copilot tool for curating cohorts from the GDC. GDC Cohort Copilot automatically generates the GDC cohort filter corresponding to a user-input natural language description of their desired cohort, before exporting the cohort back to the GDC for further analysis. An interactive user interface allows users to further refine the generated cohort. We develop and evaluate multiple large language models (LLMs) for GDC Cohort Copilot and demonstrate that our locally-served, open-source GDC Cohort LLM achieves better results than GPT-4o prompting in generating GDC cohorts. Availability and implementation: The standalone docker image for GDC Cohort Copilot is available at https://quay.io/repository/cdis/gdc-cohort-copilot. Source code is available at https://github.com/uc-cdis/gdc-cohort-copilot. GDC Cohort LLM weights are available at https://huggingface.co/uc-ctds.
|
However, users may be better able to describe their desired cohort in free-text natural language.
|
https://arxiv.org/abs/2507.02221v1
|
https://arxiv.org/pdf/2507.02221v1.pdf
| null |
[
"Steven Song",
"Anirudh Subramanyam",
"Zhenyu Zhang",
"Aarti Venkat",
"Robert L. Grossman"
] |
[] | 2025-07-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/advancing-magnetic-materials-discovery-a
|
2507.01913
| null | null |
Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction
|
Accurately predicting magnetic behavior across diverse materials systems remains a longstanding challenge due to the complex interplay of structural and electronic factors and is pivotal for the accelerated discovery and design of next-generation magnetic materials. In this work, a refined descriptor is proposed that significantly improves the prediction of two critical magnetic properties -- magnetic ordering (Ferromagnetic vs. Ferrimagnetic) and magnetic moment per atom -- using only the structural information of materials. Unlike previous models limited to Mn-based or lanthanide-transition metal compounds, the present approach generalizes across a diverse dataset of 5741 stable, binary and ternary, ferromagnetic and ferrimagnetic compounds sourced from the Materials Project. Leveraging an enriched elemental vector representation and advanced feature engineering, including nonlinear terms and reduced matrix sparsity, the LightGBM-based model achieves an accuracy of 82.4% for magnetic ordering classification and balanced recall across FM and FiM classes, addressing a key limitation in prior studies. The model predicts magnetic moment per atom with a correlation coefficient of 0.93, surpassing the Hund's matrix and orbital field matrix descriptors. Additionally, it accurately estimates formation energy per atom, enabling assessment of both magnetic behavior and material stability. This generalized and computationally efficient framework offers a robust tool for high-throughput screening of magnetic materials with tailored properties.
| null |
https://arxiv.org/abs/2507.01913v1
|
https://arxiv.org/pdf/2507.01913v1.pdf
| null |
[
"Apoorv Verma",
"Junaid Jami",
"Amrita Bhattacharya"
] |
[
"Feature Engineering",
"Formation Energy"
] | 2025-07-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/shapeembed-a-self-supervised-learning
|
2507.01009
| null | null |
ShapeEmbed: a self-supervised learning framework for 2D contour quantification
|
The shape of objects is an important source of visual information in a wide range of applications. One of the core challenges of shape quantification is to ensure that the extracted measurements remain invariant to transformations that preserve an object's intrinsic geometry, such as changing its size, orientation, and position in the image. In this work, we introduce ShapeEmbed, a self-supervised representation learning framework designed to encode the contour of objects in 2D images, represented as a Euclidean distance matrix, into a shape descriptor that is invariant to translation, scaling, rotation, reflection, and point indexing. Our approach overcomes the limitations of traditional shape descriptors while improving upon existing state-of-the-art autoencoder-based approaches. We demonstrate that the descriptors learned by our framework outperform their competitors in shape classification tasks on natural and biological images. We envision our approach to be of particular relevance to biological imaging applications.
| null |
https://arxiv.org/abs/2507.01009v1
|
https://arxiv.org/pdf/2507.01009v1.pdf
| null |
[
"Anna Foix Romero",
"Craig Russell",
"Alexander Krull",
"Virginie Uhlmann"
] |
[
"Representation Learning",
"Self-Supervised Learning"
] | 2025-07-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/robotic-manipulation-by-imitating-generated
|
2507.00990
| null | null |
Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations
|
This work introduces Robots Imitating Generated Videos (RIGVid), a system that enables robots to perform complex manipulation tasks--such as pouring, wiping, and mixing--purely by imitating AI-generated videos, without requiring any physical demonstrations or robot-specific training. Given a language command and an initial scene image, a video diffusion model generates potential demonstration videos, and a vision-language model (VLM) automatically filters out results that do not follow the command. A 6D pose tracker then extracts object trajectories from the video, and the trajectories are retargeted to the robot in an embodiment-agnostic fashion. Through extensive real-world evaluations, we show that filtered generated videos are as effective as real demonstrations, and that performance improves with generation quality. We also show that relying on generated videos outperforms more compact alternatives such as keypoint prediction using VLMs, and that strong 6D pose tracking outperforms other ways to extract trajectories, such as dense feature point tracking. These findings suggest that videos produced by a state-of-the-art off-the-shelf model can offer an effective source of supervision for robotic manipulation.
| null |
https://arxiv.org/abs/2507.00990v2
|
https://arxiv.org/pdf/2507.00990v2.pdf
| null |
[
"Shivansh Patel",
"Shraddhaa Mohan",
"Hanlin Mai",
"Unnat Jain",
"Svetlana Lazebnik",
"Yunzhu Li"
] |
[
"Point Tracking",
"Pose Tracking"
] | 2025-07-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/hineus-high-fidelity-neural-surface
|
2506.23854
| null | null |
HiNeuS: High-fidelity Neural Surface Mitigating Low-texture and Reflective Ambiguity
|
Neural surface reconstruction faces persistent challenges in reconciling geometric fidelity with photometric consistency under complex scene conditions. We present HiNeuS, a unified framework that holistically addresses three core limitations in existing approaches: multi-view radiance inconsistency, missing keypoints in textureless regions, and structural degradation from over-enforced Eikonal constraints during joint optimization. To resolve these issues through a unified pipeline, we introduce: 1) Differential visibility verification through SDF-guided ray tracing, resolving reflection ambiguities via continuous occlusion modeling; 2) Planar-conformal regularization via ray-aligned geometry patches that enforce local surface coherence while preserving sharp edges through adaptive appearance weighting; and 3) Physically-grounded Eikonal relaxation that dynamically modulates geometric constraints based on local radiance gradients, enabling detail preservation without sacrificing global regularity. Unlike prior methods that handle these aspects through sequential optimizations or isolated modules, our approach achieves cohesive integration where appearance-geometry constraints evolve synergistically throughout training. Comprehensive evaluations across synthetic and real-world datasets demonstrate state-of-the-art performance, including a 21.4% reduction in Chamfer distance over reflection-aware baselines and 2.32 dB PSNR improvement against neural rendering counterparts. Qualitative analyses reveal superior capability in recovering specular instruments, urban layouts with centimeter-scale infrastructure, and low-textured surfaces without local patch collapse. The method's generalizability is further validated through successful application to inverse rendering tasks, including material decomposition and view-consistent relighting.
| null |
https://arxiv.org/abs/2506.23854v1
|
https://arxiv.org/pdf/2506.23854v1.pdf
| null |
[
"Yida Wang",
"Xueyang Zhang",
"Kun Zhan",
"Peng Jia",
"Xianpeng Lang"
] |
[
"Inverse Rendering",
"Neural Rendering",
"Surface Reconstruction"
] | 2025-06-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/semfaceedit-semantic-face-editing-on
|
2506.22833
| null | null |
SemFaceEdit: Semantic Face Editing on Generative Radiance Manifolds
|
Despite multiple view consistency offered by 3D-aware GAN techniques, the resulting images often lack the capacity for localized editing. In response, generative radiance manifolds emerge as an efficient approach for constrained point sampling within volumes, effectively reducing computational demands and enabling the learning of fine details. This work introduces SemFaceEdit, a novel method that streamlines the appearance and geometric editing process by generating semantic fields on generative radiance manifolds. Utilizing latent codes, our method effectively disentangles the geometry and appearance associated with different facial semantics within the generated image. In contrast to existing methods that can change the appearance of the entire radiance field, our method enables the precise editing of particular facial semantics while preserving the integrity of other regions. Our network comprises two key modules: the Geometry module, which generates semantic radiance and occupancy fields, and the Appearance module, which is responsible for predicting RGB radiance. We jointly train both modules in adversarial settings to learn semantic-aware geometry and appearance descriptors. The appearance descriptors are then conditioned on their respective semantic latent codes by the Appearance Module, facilitating disentanglement and enhanced control. Our experiments highlight SemFaceEdit's superior performance in semantic field-based editing, particularly in achieving improved radiance field disentanglement.
| null |
https://arxiv.org/abs/2506.22833v1
|
https://arxiv.org/pdf/2506.22833v1.pdf
| null |
[
"Shashikant Verma",
"Shanmuganathan Raman"
] |
[
"Disentanglement"
] | 2025-06-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deterministic-object-pose-confidence-region
|
2506.22720
| null | null |
Deterministic Object Pose Confidence Region Estimation
|
6D pose confidence region estimation has emerged as a critical direction, aiming to perform uncertainty quantification for assessing the reliability of estimated poses. However, current sampling-based approach suffers from critical limitations that severely impede their practical deployment: 1) the sampling speed significantly decreases as the number of samples increases. 2) the derived confidence regions are often excessively large. To address these challenges, we propose a deterministic and efficient method for estimating pose confidence regions. Our approach uses inductive conformal prediction to calibrate the deterministically regressed Gaussian keypoint distributions into 2D keypoint confidence regions. We then leverage the implicit function theorem to propagate these keypoint confidence regions directly into 6D pose confidence regions. This method avoids the inefficiency and inflated region sizes associated with sampling and ensembling. It provides compact confidence regions that cover the ground-truth poses with a user-defined confidence level. Experimental results on the LineMOD Occlusion and SPEED datasets show that our method achieves higher pose estimation accuracy with reduced computational time. For the same coverage rate, our method yields significantly smaller confidence region volumes, reducing them by up to 99.9\% for rotations and 99.8\% for translations. The code will be available soon.
| null |
https://arxiv.org/abs/2506.22720v1
|
https://arxiv.org/pdf/2506.22720v1.pdf
| null |
[
"Jinghao Wang",
"Zhang Li",
"Zi Wang",
"Banglei Guan",
"Yang Shang",
"Qifeng Yu"
] |
[
"Conformal Prediction",
"Object",
"Pose Estimation",
"Uncertainty Quantification"
] | 2025-06-28T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/matcha-cross-algorithm-matching-with-feature
|
2506.22336
| null | null |
MatChA: Cross-Algorithm Matching with Feature Augmentation
|
State-of-the-art methods fail to solve visual localization in scenarios where different devices use different sparse feature extraction algorithms to obtain keypoints and their corresponding descriptors. Translating feature descriptors is enough to enable matching. However, performance is drastically reduced in cross-feature detector cases, because current solutions assume common keypoints. This means that the same detector has to be used, which is rarely the case in practice when different descriptors are used. The low repeatability of keypoints, in addition to non-discriminatory and non-distinctive descriptors, make the identification of true correspondences extremely challenging. We present the first method tackling this problem, which performs feature descriptor augmentation targeting cross-detector feature matching, and then feature translation to a latent space. We show that our method significantly improves image matching and visual localization in the cross-feature scenario and evaluate the proposed method on several benchmarks.
| null |
https://arxiv.org/abs/2506.22336v1
|
https://arxiv.org/pdf/2506.22336v1.pdf
| null |
[
"Paula Carbó Cubero",
"Alberto Jaenal Gálvez",
"André Mateus",
"José Araújo",
"Patric Jensfelt"
] |
[
"Visual Localization"
] | 2025-06-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ds-gt-at-checkthat-2025-ensemble-methods-for
|
2507.06205
| null | null |
DS@GT at CheckThat! 2025: Ensemble Methods for Detection of Scientific Discourse on Social Media
|
In this paper, we, as the DS@GT team for CLEF 2025 CheckThat! Task 4a Scientific Web Discourse Detection, present the methods we explored for this task. For this multiclass classification task, we determined if a tweet contained a scientific claim, a reference to a scientific study or publication, and/or mentions of scientific entities, such as a university or a scientist. We present 3 modeling approaches for this task: transformer finetuning, few-shot prompting of LLMs, and a combined ensemble model whose design was informed by earlier experiments. Our team placed 7th in the competition, achieving a macro-averaged F1 score of 0.8611, an improvement over the DeBERTaV3 baseline of 0.8375. Our code is available on Github at https://github.com/dsgt-arc/checkthat-2025-swd/tree/main/subtask-4a.
|
In this paper, we, as the DS@GT team for CLEF 2025 CheckThat!
|
https://arxiv.org/abs/2507.06205v1
|
https://arxiv.org/pdf/2507.06205v1.pdf
| null |
[
"Ayush Parikh",
"Hoang Thanh Thanh Truong",
"Jeanette Schofield",
"Maximilian Heil"
] |
[
"ARC"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ds-gt-at-checkthat-2025-evaluating-context
|
2507.06195
| null | null |
DS@GT at CheckThat! 2025: Evaluating Context and Tokenization Strategies for Numerical Fact Verification
|
Numerical claims, statements involving quantities, comparisons, and temporal references, pose unique challenges for automated fact-checking systems. In this study, we evaluate modeling strategies for veracity prediction of such claims using the QuanTemp dataset and building our own evidence retrieval pipeline. We investigate three key factors: (1) the impact of more evidences with longer input context windows using ModernBERT, (2) the effect of right-to-left (R2L) tokenization, and (3) their combined influence on classification performance. Contrary to prior findings in arithmetic reasoning tasks, R2L tokenization does not boost natural language inference (NLI) of numerical tasks. A longer context window does also not enhance veracity performance either, highlighting evidence quality as the dominant bottleneck. Our best-performing system achieves competitive macro-average F1 score of 0.57 and places us among the Top-4 submissions in Task 3 of CheckThat! 2025. Our code is available at https://github.com/dsgt-arc/checkthat-2025-numerical.
|
Numerical claims, statements involving quantities, comparisons, and temporal references, pose unique challenges for automated fact-checking systems.
|
https://arxiv.org/abs/2507.06195v1
|
https://arxiv.org/pdf/2507.06195v1.pdf
| null |
[
"Maximilian Heil",
"Aleksandar Pramov"
] |
[
"ARC",
"Arithmetic Reasoning",
"Fact Checking",
"Fact Verification",
"Natural Language Inference"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/softremish-a-novel-activation-function-for
|
2507.06148
| null | null |
SoftReMish: A Novel Activation Function for Enhanced Convolutional Neural Networks for Visual Recognition Performance
|
In this study, SoftReMish, a new activation function designed to improve the performance of convolutional neural networks (CNNs) in image classification tasks, is proposed. Using the MNIST dataset, a standard CNN architecture consisting of two convolutional layers, max pooling, and fully connected layers was implemented. SoftReMish was evaluated against popular activation functions including ReLU, Tanh, and Mish by replacing the activation function in all trainable layers. The model performance was assessed in terms of minimum training loss and maximum validation accuracy. Results showed that SoftReMish achieved a minimum loss (3.14e-8) and a validation accuracy (99.41%), outperforming all other functions tested. These findings demonstrate that SoftReMish offers better convergence behavior and generalization capability, making it a promising candidate for visual recognition tasks.
| null |
https://arxiv.org/abs/2507.06148v1
|
https://arxiv.org/pdf/2507.06148v1.pdf
| null |
[
"Mustafa Bayram Gücen"
] |
[
"image-classification",
"Image Classification"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I File a Claim with Expedia?\r\nCall **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Fast Help & Exclusive Travel Discounts!Need to file a claim with Expedia? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now for immediate support and unlock exclusive best deal offers on hotels, flights, and vacation packages. Resolve your issue quickly while enjoying limited-time travel discounts that make your next trip smoother, more affordable, and worry-free. Don’t miss out—call today and save!\r\n.How do I get a full refund from Expedia?\r\nHow Do I Communicate with Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for 24/7 Support & Exclusive Travel Discounts!Need to reach Expedia fast? Call now to speak directly with a live agent and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get personalized assistance while enjoying limited-time travel offers that make your next journey smoother, more affordable, and stress-free. Don’t wait—call today and save!",
"full_name": "(TravEL!!Guide)How Do I File a Claim with Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "(TravEL!!Guide)How Do I File a Claim with Expedia?",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
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"full_name": "+ ( 1 ) ⟷ 888 ⟷ ( 829 ) ⟷ 0881 How do I file a claim with Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "+ ( 1 ) ⟷ 888 ⟷ ( 829 ) ⟷ 0881 How do I file a claim with Expedia?",
"source_title": "Mish: A Self Regularized Non-Monotonic Activation Function",
"source_url": "https://arxiv.org/abs/1908.08681v3"
}
] |
https://paperswithcode.com/paper/prompt-free-conditional-diffusion-for-multi
|
2507.06146
| null | null |
Prompt-Free Conditional Diffusion for Multi-object Image Augmentation
|
Diffusion models has underpinned much recent advances of dataset augmentation in various computer vision tasks. However, when involving generating multi-object images as real scenarios, most existing methods either rely entirely on text condition, resulting in a deviation between the generated objects and the original data, or rely too much on the original images, resulting in a lack of diversity in the generated images, which is of limited help to downstream tasks. To mitigate both problems with one stone, we propose a prompt-free conditional diffusion framework for multi-object image augmentation. Specifically, we introduce a local-global semantic fusion strategy to extract semantics from images to replace text, and inject knowledge into the diffusion model through LoRA to alleviate the category deviation between the original model and the target dataset. In addition, we design a reward model based counting loss to assist the traditional reconstruction loss for model training. By constraining the object counts of each category instead of pixel-by-pixel constraints, bridging the quantity deviation between the generated data and the original data while improving the diversity of the generated data. Experimental results demonstrate the superiority of the proposed method over several representative state-of-the-art baselines and showcase strong downstream task gain and out-of-domain generalization capabilities. Code is available at \href{https://github.com/00why00/PFCD}{here}.
|
Specifically, we introduce a local-global semantic fusion strategy to extract semantics from images to replace text, and inject knowledge into the diffusion model through LoRA to alleviate the category deviation between the original model and the target dataset.
|
https://arxiv.org/abs/2507.06146v1
|
https://arxiv.org/pdf/2507.06146v1.pdf
| null |
[
"Haoyu Wang",
"Lei Zhang",
"Wei Wei",
"Chen Ding",
"Yanning Zhang"
] |
[
"Diversity",
"Domain Generalization",
"Image Augmentation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/neobabel-a-multilingual-open-tower-for-visual
|
2507.06137
| null | null |
NeoBabel: A Multilingual Open Tower for Visual Generation
|
Text-to-image generation advancements have been predominantly English-centric, creating barriers for non-English speakers and perpetuating digital inequities. While existing systems rely on translation pipelines, these introduce semantic drift, computational overhead, and cultural misalignment. We introduce NeoBabel, a novel multilingual image generation framework that sets a new Pareto frontier in performance, efficiency and inclusivity, supporting six languages: English, Chinese, Dutch, French, Hindi, and Persian. The model is trained using a combination of large-scale multilingual pretraining and high-resolution instruction tuning. To evaluate its capabilities, we expand two English-only benchmarks to multilingual equivalents: m-GenEval and m-DPG. NeoBabel achieves state-of-the-art multilingual performance while retaining strong English capability, scoring 0.75 on m-GenEval and 0.68 on m-DPG. Notably, it performs on par with leading models on English tasks while outperforming them by +0.11 and +0.09 on multilingual benchmarks, even though these models are built on multilingual base LLMs. This demonstrates the effectiveness of our targeted alignment training for preserving and extending crosslingual generalization. We further introduce two new metrics to rigorously assess multilingual alignment and robustness to code-mixed prompts. Notably, NeoBabel matches or exceeds English-only models while being 2-4x smaller. We release an open toolkit, including all code, model checkpoints, a curated dataset of 124M multilingual text-image pairs, and standardized multilingual evaluation protocols, to advance inclusive AI research. Our work demonstrates that multilingual capability is not a trade-off but a catalyst for improved robustness, efficiency, and cultural fidelity in generative AI.
|
Notably, it performs on par with leading models on English tasks while outperforming them by +0. 11 and +0. 09 on multilingual benchmarks, even though these models are built on multilingual base LLMs.
|
https://arxiv.org/abs/2507.06137v1
|
https://arxiv.org/pdf/2507.06137v1.pdf
| null |
[
"Mohammad Mahdi Derakhshani",
"Dheeraj Varghese",
"Marzieh Fadaee",
"Cees G. M. Snoek"
] |
[
"Image Generation",
"Text to Image Generation",
"Text-to-Image Generation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
}
] |
https://paperswithcode.com/paper/a-linear-generative-framework-for-structure
|
2507.06136
| null | null |
A Linear Generative Framework for Structure-Function Coupling in the Human Brain
|
Brain function emerges from coordinated activity across anatomically connected regions, where structural connectivity (SC) -- the network of white matter pathways - provides the physical substrate for functional connectivity (FC) -- the correlated neural activity between brain areas. While these structural and functional networks exhibit substantial overlap, their relationship involves complex, indirect mechanisms, including the dynamic interplay of direct and indirect pathways, recurrent network interactions, and neuromodulatory influences. To systematically untangle how structural architecture shapes functional patterns, this work aims to establish a set of rules that decode how direct and indirect structural connections and motifs give rise to FC between brain regions. Specifically, using a generative linear model, we derive explicit rules that predict an individual's resting-state fMRI FC from diffusion-weighted imaging (DWI)-derived SC, validated against topological null models. Examining the rules reveals distinct classes of brain regions, with integrator hubs acting as structural linchpins promoting synchronization and mediator hubs serving as structural fulcrums orchestrating competing dynamics. Through virtual lesion experiments, we demonstrate how different cortical and subcortical systems distinctively contribute to global functional organization. Together, this framework disentangles the mechanisms by which structural architecture drives functional dynamics, enabling the prediction of how pathological or surgical disruptions to brain connectivity cascade through functional networks, potentially leading to cognitive and behavioral impairments.
| null |
https://arxiv.org/abs/2507.06136v1
|
https://arxiv.org/pdf/2507.06136v1.pdf
| null |
[
"Sam Frank Kelemen",
"Joaquín Gõni",
"Sérgio Pequito",
"Arian Ashourvan"
] |
[
"Functional Connectivity"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/speech-quality-assessment-model-based-on
|
2507.06116
| null | null |
Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
|
Automatic speech quality assessment plays a crucial role in the development of speech synthesis systems, but existing models exhibit significant performance variations across different granularity levels of prediction tasks. This paper proposes an enhanced MOS prediction system based on self-supervised learning speech models, incorporating a Mixture of Experts (MoE) classification head and utilizing synthetic data from multiple commercial generation models for data augmentation. Our method builds upon existing self-supervised models such as wav2vec2, designing a specialized MoE architecture to address different types of speech quality assessment tasks. We also collected a large-scale synthetic speech dataset encompassing the latest text-to-speech, speech conversion, and speech enhancement systems. However, despite the adoption of the MoE architecture and expanded dataset, the model's performance improvements in sentence-level prediction tasks remain limited. Our work reveals the limitations of current methods in handling sentence-level quality assessment, provides new technical pathways for the field of automatic speech quality assessment, and also delves into the fundamental causes of performance differences across different assessment granularities.
| null |
https://arxiv.org/abs/2507.06116v1
|
https://arxiv.org/pdf/2507.06116v1.pdf
| null |
[
"Xintong Hu",
"Yixuan Chen",
"Rui Yang",
"Wenxiang Guo",
"Changhao Pan"
] |
[
"Data Augmentation",
"Mixture-of-Experts",
"Prediction",
"Self-Supervised Learning",
"Sentence",
"Speech Enhancement",
"Speech Synthesis",
"text-to-speech",
"Text to Speech"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Mixture of Experts",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Ensembling",
"parent": null
},
"name": "MoE",
"source_title": "Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs",
"source_url": "https://arxiv.org/abs/2403.07743v3"
}
] |
https://paperswithcode.com/paper/lighthousegs-indoor-structure-aware-3d
|
2507.06109
| null | null |
LighthouseGS: Indoor Structure-aware 3D Gaussian Splatting for Panorama-Style Mobile Captures
|
Recent advances in 3D Gaussian Splatting (3DGS) have enabled real-time novel view synthesis (NVS) with impressive quality in indoor scenes. However, achieving high-fidelity rendering requires meticulously captured images covering the entire scene, limiting accessibility for general users. We aim to develop a practical 3DGS-based NVS framework using simple panorama-style motion with a handheld camera (e.g., mobile device). While convenient, this rotation-dominant motion and narrow baseline make accurate camera pose and 3D point estimation challenging, especially in textureless indoor scenes. To address these challenges, we propose LighthouseGS, a novel framework inspired by the lighthouse-like sweeping motion of panoramic views. LighthouseGS leverages rough geometric priors, such as mobile device camera poses and monocular depth estimation, and utilizes the planar structures often found in indoor environments. We present a new initialization method called plane scaffold assembly to generate consistent 3D points on these structures, followed by a stable pruning strategy to enhance geometry and optimization stability. Additionally, we introduce geometric and photometric corrections to resolve inconsistencies from motion drift and auto-exposure in mobile devices. Tested on collected real and synthetic indoor scenes, LighthouseGS delivers photorealistic rendering, surpassing state-of-the-art methods and demonstrating the potential for panoramic view synthesis and object placement.
| null |
https://arxiv.org/abs/2507.06109v1
|
https://arxiv.org/pdf/2507.06109v1.pdf
| null |
[
"Seungoh Han",
"Jaehoon Jang",
"Hyunsu Kim",
"Jaeheung Surh",
"Junhyung Kwak",
"Hyowon Ha",
"Kyungdon Joo"
] |
[
"3DGS",
"Depth Estimation",
"Monocular Depth Estimation",
"Novel View Synthesis"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Pruning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Model Compression",
"parent": null
},
"name": "Pruning",
"source_title": "Pruning Filters for Efficient ConvNets",
"source_url": "http://arxiv.org/abs/1608.08710v3"
}
] |
https://paperswithcode.com/paper/miniaturized-optically-generated-bessel-beam
|
2507.06108
| null | null |
Miniaturized optically-generated Bessel beam ultrasound for volumetric transcranial brain stimulation
|
Non-invasive stimulation of small, variably shaped brain sub-regions is crucial for advancing our understanding of brain functions. Current ultrasound neuromodulation faces two significant trade-offs when targeting brain sub-regions: miniaturization versus volumetric control and spatial resolution versus transcranial capability. Here, we present an optically-generated Bessel beam ultrasound (OBUS) device designed to overcome these limitations. This 2.33 mm-diameter miniaturized device delivers a column-shaped field achieving a lateral resolution of 152 um and an axial resolution of 1.93 mm, targeting brain sub-regions with an elongated volume of tissue activation. Immunofluorescence imaging of mouse brain slices confirms its ability to stimulate cells at a depth of 2.2 mm. Additionally, OBUS outperforms conventional Gaussian ultrasound in transcranial transmission efficiency and beam shape preservation. Electrophysiological recordings and functional MRI captured rodent brain responses evoked by OBUS, demonstrating OBUS's ability to non-invasively activate neural circuits in intact brains. This technology offers new possibilities for studying brain functions with precision and volumetric control.
| null |
https://arxiv.org/abs/2507.06108v1
|
https://arxiv.org/pdf/2507.06108v1.pdf
| null |
[
"Yueming Li",
"Guo Chen",
"Tiago R. Oliveira",
"Nick Todd",
"Yong-Zhi Zhang",
"Carolyn Marar",
"Nan Zheng",
"Lu Lan",
"Nathan McDannold",
"Ji-Xin Cheng",
"Chen Yang"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/tile-based-vit-inference-with-visual-cluster
|
2507.06093
| null | null |
Tile-Based ViT Inference with Visual-Cluster Priors for Zero-Shot Multi-Species Plant Identification
|
We describe DS@GT's second-place solution to the PlantCLEF 2025 challenge on multi-species plant identification in vegetation quadrat images. Our pipeline combines (i) a fine-tuned Vision Transformer ViTD2PC24All for patch-level inference, (ii) a 4x4 tiling strategy that aligns patch size with the network's 518x518 receptive field, and (iii) domain-prior adaptation through PaCMAP + K-Means visual clustering and geolocation filtering. Tile predictions are aggregated by majority vote and re-weighted with cluster-specific Bayesian priors, yielding a macro-averaged F1 of 0.348 (private leaderboard) while requiring no additional training. All code, configuration files, and reproducibility scripts are publicly available at https://github.com/dsgt-arc/plantclef-2025.
|
We describe DS@GT's second-place solution to the PlantCLEF 2025 challenge on multi-species plant identification in vegetation quadrat images.
|
https://arxiv.org/abs/2507.06093v1
|
https://arxiv.org/pdf/2507.06093v1.pdf
| null |
[
"Murilo Gustineli",
"Anthony Miyaguchi",
"Adrian Cheung",
"Divyansh Khattak"
] |
[
"ARC"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google-research/vision_transformer",
"description": "The **Vision Transformer**, or **ViT**, is a model for image classification that employs a [Transformer](https://paperswithcode.com/method/transformer)-like architecture over patches of the image. An image is split into fixed-size patches, each of them are then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to a standard [Transformer](https://paperswithcode.com/method/transformer) encoder. In order to perform classification, the standard approach of adding an extra learnable “classification token” to the sequence is used.",
"full_name": "Vision Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images for downstream tasks such as classification and object detection. The most popular subcategory are convolutional neural networks. Below you can find a continuously updated list of image models.",
"name": "Image Models",
"parent": null
},
"name": "Vision Transformer",
"source_title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
"source_url": "https://arxiv.org/abs/2010.11929v2"
}
] |
https://paperswithcode.com/paper/scoreadv-score-based-targeted-generation-of
|
2507.06078
| null | null |
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion Models
|
Despite the success of deep learning across various domains, it remains vulnerable to adversarial attacks. Although many existing adversarial attack methods achieve high success rates, they typically rely on $\ell_{p}$-norm perturbation constraints, which do not align with human perceptual capabilities. Consequently, researchers have shifted their focus toward generating natural, unrestricted adversarial examples (UAEs). GAN-based approaches suffer from inherent limitations, such as poor image quality due to instability and mode collapse. Meanwhile, diffusion models have been employed for UAE generation, but they still rely on iterative PGD perturbation injection, without fully leveraging their central denoising capabilities. In this paper, we introduce a novel approach for generating UAEs based on diffusion models, named ScoreAdv. This method incorporates an interpretable adversarial guidance mechanism to gradually shift the sampling distribution towards the adversarial distribution, while using an interpretable saliency map to inject the visual information of a reference image into the generated samples. Notably, our method is capable of generating an unlimited number of natural adversarial examples and can attack not only classification models but also retrieval models. We conduct extensive experiments on ImageNet and CelebA datasets, validating the performance of ScoreAdv across ten target models in both black-box and white-box settings. Our results demonstrate that ScoreAdv achieves state-of-the-art attack success rates and image quality. Furthermore, the dynamic balance between denoising and adversarial perturbation enables ScoreAdv to remain robust even under defensive measures.
|
In this paper, we introduce a novel approach for generating UAEs based on diffusion models, named ScoreAdv.
|
https://arxiv.org/abs/2507.06078v1
|
https://arxiv.org/pdf/2507.06078v1.pdf
| null |
[
"Chihan Huang",
"Hao Tang"
] |
[
"Adversarial Attack",
"Denoising"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In the ALIGN method, visual and language representations are jointly trained from noisy image alt-text data. The image and text encoders are learned via contrastive loss (formulated as normalized softmax) that pushes the embeddings of the matched image-text pair together and pushing those of non-matched image-text pair apart. The model learns to align visual and language representations of the image and text pairs using the contrastive loss. The representations can be used for vision-only or vision-language task transfer. Without any fine-tuning, ALIGN powers zero-shot visual classification and cross-modal search including image-to-text search, text-to image search and even search with joint image+text queries.",
"full_name": "ALIGN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "Involves models that adapt pre-training to the field of Vision-and-Language (V-L) learning and improve the performance on downstream tasks like visual question answering and visual captioning.\r\n\r\nAccording to [Du et al. (2022)](https://arxiv.org/pdf/2202.10936.pdf), information coming from the different modalities can be encoded in three ways: fusion encoder, dual encoder, and a combination of both. \r\n\r\nReferences:\r\n\r\n- [A Survey of Vision-Language Pre-Trained Models](https://arxiv.org/pdf/2202.10936.pdf)\r\n- [Vision Language models: towards multi-modal deep learning](https://theaisummer.com/vision-language-models/)",
"name": "Vision and Language Pre-Trained Models",
"parent": null
},
"name": "ALIGN",
"source_title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision",
"source_url": "https://arxiv.org/abs/2102.05918v2"
},
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/enhancing-synthetic-ct-from-cbct-via-1
|
2507.06067
| null | null |
Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration
|
Cone-Beam Computed Tomography (CBCT) is widely used for intraoperative imaging due to its rapid acquisition and low radiation dose. However, CBCT images typically suffer from artifacts and lower visual quality compared to conventional Computed Tomography (CT). A promising solution is synthetic CT (sCT) generation, where CBCT volumes are translated into the CT domain. In this work, we enhance sCT generation through multimodal learning by jointly leveraging intraoperative CBCT and preoperative CT data. To overcome the inherent misalignment between modalities, we introduce an end-to-end learnable registration module within the sCT pipeline. This model is evaluated on a controlled synthetic dataset, allowing precise manipulation of data quality and alignment parameters. Further, we validate its robustness and generalizability on two real-world clinical datasets. Experimental results demonstrate that integrating registration in multimodal sCT generation improves sCT quality, outperforming baseline multimodal methods in 79 out of 90 evaluation settings. Notably, the improvement is most significant in cases where CBCT quality is low and the preoperative CT is moderately misaligned.
|
A promising solution is synthetic CT (sCT) generation, where CBCT volumes are translated into the CT domain.
|
https://arxiv.org/abs/2507.06067v1
|
https://arxiv.org/pdf/2507.06067v1.pdf
| null |
[
"Maximilian Tschuchnig",
"Lukas Lamminger",
"Philipp Steininger",
"Michael Gadermayr"
] |
[
"Computed Tomography (CT)"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/estimating-prevalence-with-precision-and
|
2507.06061
| null | null |
Estimating prevalence with precision and accuracy
|
Unlike classification, whose goal is to estimate the class of each data point in a dataset, prevalence estimation or quantification is a task that aims to estimate the distribution of classes in a dataset. The two main tasks in prevalence estimation are to adjust for bias, due to the prevalence in the training dataset, and to quantify the uncertainty in the estimate. The standard methods used to quantify uncertainty in prevalence estimates are bootstrapping and Bayesian quantification methods. It is not clear which approach is ideal in terms of precision (i.e. the width of confidence intervals) and coverage (i.e. the confidence intervals being well-calibrated). Here, we propose Precise Quantifier (PQ), a Bayesian quantifier that is more precise than existing quantifiers and with well-calibrated coverage. We discuss the theory behind PQ and present experiments based on simulated and real-world datasets. Through these experiments, we establish the factors which influence quantification precision: the discriminatory power of the underlying classifier; the size of the labeled dataset used to train the quantifier; and the size of the unlabeled dataset for which prevalence is estimated. Our analysis provides deep insights into uncertainty quantification for quantification learning.
|
Unlike classification, whose goal is to estimate the class of each data point in a dataset, prevalence estimation or quantification is a task that aims to estimate the distribution of classes in a dataset.
|
https://arxiv.org/abs/2507.06061v1
|
https://arxiv.org/pdf/2507.06061v1.pdf
| null |
[
"Aime Bienfait Igiraneza",
"Christophe Fraser",
"Robert Hinch"
] |
[
"Uncertainty Quantification"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/textpixs-glyph-conditioned-diffusion-with
|
2507.06033
| null | null |
TextPixs: Glyph-Conditioned Diffusion with Character-Aware Attention and OCR-Guided Supervision
|
The modern text-to-image diffusion models boom has opened a new era in digital content production as it has proven the previously unseen ability to produce photorealistic and stylistically diverse imagery based on the semantics of natural-language descriptions. However, the consistent disadvantage of these models is that they cannot generate readable, meaningful, and correctly spelled text in generated images, which significantly limits the use of practical purposes like advertising, learning, and creative design. This paper introduces a new framework, namely Glyph-Conditioned Diffusion with Character-Aware Attention (GCDA), using which a typical diffusion backbone is extended by three well-designed modules. To begin with, the model has a dual-stream text encoder that encodes both semantic contextual information and explicit glyph representations, resulting in a character-aware representation of the input text that is rich in nature. Second, an attention mechanism that is aware of the character is proposed with a new attention segregation loss that aims to limit the attention distribution of each character independently in order to avoid distortion artifacts. Lastly, GCDA has an OCR-in-the-loop fine-tuning phase, where a full text perceptual loss, directly optimises models to be legible and accurately spell. Large scale experiments to benchmark datasets, such as MARIO-10M and T2I-CompBench, reveal that GCDA sets a new state-of-the-art on all metrics, with better character based metrics on text rendering (Character Error Rate: 0.08 vs 0.21 for the previous best; Word Error Rate: 0.15 vs 0.25), human perception, and comparable image synthesis quality on high-fidelity (FID: 14.3).
| null |
https://arxiv.org/abs/2507.06033v1
|
https://arxiv.org/pdf/2507.06033v1.pdf
| null |
[
"Syeda Anshrah Gillani",
"Mirza Samad Ahmed Baig",
"Osama Ahmed Khan",
"Shahid Munir Shah",
"Umema Mujeeb",
"Maheen Ali"
] |
[
"Image Generation",
"Optical Character Recognition (OCR)"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
},
{
"code_snippet_url": null,
"description": "We propose to theoretically and empirically examine the effect of incorporating weighting schemes into walk-aggregating GNNs. To this end, we propose a simple, interpretable, and end-to-end supervised GNN model, called AWARE (Attentive Walk-Aggregating GRaph Neural NEtwork), for graph-level prediction. AWARE aggregates the walk information by means of weighting schemes at distinct levels (vertex-, walk-, and graph-level) in a principled manner. By virtue of the incorporated weighting schemes at these different levels, AWARE can emphasize the information important for prediction while diminishing the irrelevant ones—leading to representations that can improve learning performance.",
"full_name": "Attentive Walk-Aggregating Graph Neural Network",
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "AWARE",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/feature-guided-neighbor-selection-for-non
|
2507.06029
| null | null |
Feature-Guided Neighbor Selection for Non-Expert Evaluation of Model Predictions
|
Explainable AI (XAI) methods often struggle to generate clear, interpretable outputs for users without domain expertise. We introduce Feature-Guided Neighbor Selection (FGNS), a post hoc method that enhances interpretability by selecting class-representative examples using both local and global feature importance. In a user study (N = 98) evaluating Kannada script classifications, FGNS significantly improved non-experts' ability to identify model errors while maintaining appropriate agreement with correct predictions. Participants made faster and more accurate decisions compared to those given traditional k-NN explanations. Quantitative analysis shows that FGNS selects neighbors that better reflect class characteristics rather than merely minimizing feature-space distance, leading to more consistent selection and tighter clustering around class prototypes. These results support FGNS as a step toward more human-aligned model assessment, although further work is needed to address the gap between explanation quality and perceived trust.
| null |
https://arxiv.org/abs/2507.06029v1
|
https://arxiv.org/pdf/2507.06029v1.pdf
| null |
[
"Courtney Ford",
"Mark T. Keane"
] |
[
"Feature Importance"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/UCSC-REAL/HOC",
"description": "",
"full_name": "High-Order Consensuses",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Value Function Estimation",
"parent": null
},
"name": "HOC",
"source_title": "Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels",
"source_url": "https://arxiv.org/abs/2102.05291v2"
},
{
"code_snippet_url": null,
"description": "**$k$-Nearest Neighbors** is a clustering-based algorithm for classification and regression. It is a a type of instance-based learning as it does not attempt to construct a general internal model, but simply stores instances of the training data. Prediction is computed from a simple majority vote of the nearest neighbors of each point: a query point is assigned the data class which has the most representatives within the nearest neighbors of the point.\r\n\r\nSource of Description and Image: [scikit-learn](https://scikit-learn.org/stable/modules/neighbors.html#classification)",
"full_name": "k-Nearest Neighbors",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "k-NN",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/a-differential-evolution-algorithm-with
|
2507.06020
| null | null |
A Differential Evolution Algorithm with Neighbor-hood Mutation for DOA Estimation
|
Two-dimensional (2D) Multiple Signal Classification algorithm is a powerful technique for high-resolution direction-of-arrival (DOA) estimation in array signal processing. However, the exhaustive search over the 2D an-gular domain leads to high computa-tional cost, limiting its applicability in real-time scenarios. In this work, we reformulate the peak-finding process as a multimodal optimization prob-lem, and propose a Differential Evolu-tion algorithm with Neighborhood Mutation (DE-NM) to efficiently lo-cate multiple spectral peaks without requiring dense grid sampling. Simu-lation results demonstrate that the proposed method achieves comparable estimation accuracy to the traditional grid search, while significantly reduc-ing computation time. This strategy presents a promising solution for real-time, high-resolution DOA estimation in practical applications. The imple-mentation code is available at https://github.com/zzb-nice/DOA_multimodel_optimize.
|
Two-dimensional (2D) Multiple Signal Classification algorithm is a powerful technique for high-resolution direction-of-arrival (DOA) estimation in array signal processing.
|
https://arxiv.org/abs/2507.06020v1
|
https://arxiv.org/pdf/2507.06020v1.pdf
| null |
[
"Bo Zhou",
"Kaijie Xu",
"Yinghui Quan",
"Mengdao Xing"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/omnipart-part-aware-3d-generation-with
|
2507.06165
| null | null |
OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesion
|
The creation of 3D assets with explicit, editable part structures is crucial for advancing interactive applications, yet most generative methods produce only monolithic shapes, limiting their utility. We introduce OmniPart, a novel framework for part-aware 3D object generation designed to achieve high semantic decoupling among components while maintaining robust structural cohesion. OmniPart uniquely decouples this complex task into two synergistic stages: (1) an autoregressive structure planning module generates a controllable, variable-length sequence of 3D part bounding boxes, critically guided by flexible 2D part masks that allow for intuitive control over part decomposition without requiring direct correspondences or semantic labels; and (2) a spatially-conditioned rectified flow model, efficiently adapted from a pre-trained holistic 3D generator, synthesizes all 3D parts simultaneously and consistently within the planned layout. Our approach supports user-defined part granularity, precise localization, and enables diverse downstream applications. Extensive experiments demonstrate that OmniPart achieves state-of-the-art performance, paving the way for more interpretable, editable, and versatile 3D content.
| null |
https://arxiv.org/abs/2507.06165v1
|
https://arxiv.org/pdf/2507.06165v1.pdf
| null |
[
"Yunhan Yang",
"Yufan Zhou",
"Yuan-Chen Guo",
"Zi-Xin Zou",
"Yukun Huang",
"Ying-Tian Liu",
"Hao Xu",
"Ding Liang",
"Yan-Pei Cao",
"Xihui Liu"
] |
[
"3D Generation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/hierarchical-interaction-summarization-and
|
2507.06044
| null | null |
Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations
|
Explainable recommendations, which use the information of user and item with interaction to generate a explanation for why the user would interact with the item, are crucial for improving user trust and decision transparency to the recommender system. Existing methods primarily rely on encoding features of users and items to embeddings, which often leads to information loss due to dimensionality reduction, sparse interactions, and so on. With the advancements of large language models (LLMs) in language comprehension, some methods use embeddings as LLM inputs for explanation generation. However, since embeddings lack inherent semantics, LLMs must adjust or extend their parameters to interpret them, a process that inevitably incurs information loss. To address this issue, we propose a novel approach combining profile generation via hierarchical interaction summarization (PGHIS), which leverages a pretrained LLM to hierarchically summarize user-item interactions, generating structured textual profiles as explicit representations of user and item characteristics. Additionally, we propose contrastive prompting for explanation generation (CPEG) which employs contrastive learning to guide another reasoning language models in producing high-quality ground truth recommendation explanations. Finally, we use the textual profiles of user and item as input and high-quality explanation as output to fine-tune a LLM for generating explanations. Experimental results on multiple datasets demonstrate that our approach outperforms existing state-of-the-art methods, achieving a great improvement on metrics about explainability (e.g., 5% on GPTScore) and text quality. Furthermore, our generated ground truth explanations achieve a significantly higher win rate compared to user-written reviews and those produced by other methods, demonstrating the effectiveness of CPEG in generating high-quality ground truths.
| null |
https://arxiv.org/abs/2507.06044v1
|
https://arxiv.org/pdf/2507.06044v1.pdf
| null |
[
"Yibin Liu",
"Ang Li",
"Shijian Li"
] |
[
"Contrastive Learning",
"Dimensionality Reduction",
"Explanation Generation",
"Profile Generation",
"Recommendation Systems"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": null,
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "Contrastive Learning",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/cognisql-r1-zero-lightweight-reinforced
|
2507.06013
| null | null |
CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation
|
Translating natural language into SQL (Text-to-SQL) remains a core challenge at the intersection of language understanding and structured data access. Although large language models (LLMs) have improved fluency, generating correct and executable SQL, especially for complex queries, continues to be challenging. We introduce CogniSQL-R1-Zero, a reinforcement learning (RL) framework and model that produces accurate SQL using a lightweight reward signal based on execution correctness and format-tag compliance. By avoiding intermediate supervision, hybrid pipelines and complex reward shaping, our method encourages stable learning and stronger alignment with the ultimate task objective-producing executable programs. CogniSQL-R1-Zero achieves state-of-the-art execution accuracy on Text2SQL benchmark; BIRD bench, outperforming prior supervised and instruction-tuned baselines including SFT CodeS-7B, DeepSeek-Coder 236B, and Mistral 123B-despite being trained on a significantly smaller 7B backbone. This result underscores the scalability and efficiency of our RL-based approach when trained on just four NVIDIA A100 GPUs (40 GB VRAM each). To support further research in efficient and interpretable Text-to-SQL modeling, we release two curated datasets: (i) a collection of 5,024 reasoning traces with varying context lengths, and (ii) a positive-sampled corpus of 36,356 corpus of weakly supervised queries, each annotated with six semantically diverse reasoning paths. Together, these contributions advance scalable, execution-aligned Text-to-SQL generation.
| null |
https://arxiv.org/abs/2507.06013v1
|
https://arxiv.org/pdf/2507.06013v1.pdf
| null |
[
"Kushal Gajjar",
"Harshit Sikchi",
"Arpit Singh Gautam",
"Marc Hammons",
"Saurabh Jha"
] |
[
"Reinforcement Learning (RL)",
"TAG",
"Text to SQL",
"Text-To-SQL"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**Shrink and Fine-Tune**, or **SFT**, is a type of distillation that avoids explicit distillation by copying parameters to a student student model and then fine-tuning. Specifically it extracts a student model from the maximally spaced layers of a fine-tuned teacher. Each layer $l \\in L'$ is copied fully from $L$. For example, when creating a [BART](https://paperswithcode.com/method/bart) student with 3 decoder layers from the 12 encoder layer 12 decoder layer teacher, we copy the teacher’s full $Enc^{L}$ and decoder layers 0, 6, and 11 to the student. When deciding which layers to copy, we break ties arbitrarily; copying layers 0, 5, and 11 might work just as well. When copy only 1 decoder layer, we copy layer 0. This was found this to work better than copying layer 11. The impact of initialization on performance is measured experimentally in Section 6.1. After initialization, the student model continues to fine-tune on the summarization dataset, with the objective of minimizing $\\mathcal{L}\\_{Data}$.",
"full_name": "Shrink and Fine-Tune",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Knowledge Distillation",
"parent": null
},
"name": "SFT",
"source_title": "Pre-trained Summarization Distillation",
"source_url": "https://arxiv.org/abs/2010.13002v2"
}
] |
https://paperswithcode.com/paper/semantic-certainty-assessment-in-vector
|
2507.05933
| null | null |
Semantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation
|
Vector retrieval systems exhibit significant performance variance across queries due to heterogeneous embedding quality. We propose a lightweight framework for predicting retrieval performance at the query level by combining quantization robustness and neighborhood density metrics. Our approach is motivated by the observation that high-quality embeddings occupy geometrically stable regions in the embedding space and exhibit consistent neighborhood structures. We evaluate our method on 4 standard retrieval datasets, showing consistent improvements of 9.4$\pm$1.2\% in Recall@10 over competitive baselines. The framework requires minimal computational overhead (less than 5\% of retrieval time) and enables adaptive retrieval strategies. Our analysis reveals systematic patterns in embedding quality across different query types, providing insights for targeted training data augmentation.
| null |
https://arxiv.org/abs/2507.05933v1
|
https://arxiv.org/pdf/2507.05933v1.pdf
| null |
[
"Y. Du"
] |
[
"Data Augmentation",
"Quantization",
"Retrieval"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/diffusion-dataset-condensation-training-your
|
2507.05914
| null | null |
Diffusion Dataset Condensation: Training Your Diffusion Model Faster with Less Data
|
Diffusion models have achieved remarkable success in various generative tasks, but training them remains highly resource-intensive, often requiring millions of images and many days of GPU computation. From a data-centric perspective addressing this limitation, we study diffusion dataset condensation as a new and challenging problem setting. The goal is to construct a "synthetic" sub-dataset with significantly fewer samples than the original dataset, enabling high-quality diffusion model training with greatly reduced cost. To the best of our knowledge, we are the first to formally investigate dataset condensation for diffusion models, whereas prior work focused on training discriminative models. To tackle this new challenge, we propose a novel Diffusion Dataset Condensation (D2C) framework, which consists of two phases: Select and Attach. The Select phase identifies a compact and diverse subset using a diffusion difficulty score and interval sampling. The Attach phase enhances the selected subset by attaching rich semantic and visual representations to strengthen the conditional signals. Extensive experiments across various dataset sizes, model architectures, and resolutions show that our D2C framework enables significantly faster diffusion model training with dramatically fewer data, while preserving high visual quality. Notably, for the SiT-XL/2 architecture, D2C achieves a 100x training speed-up, reaching a FID score of 4.3 in just 40k steps using only 0.8% of the training data.
| null |
https://arxiv.org/abs/2507.05914v1
|
https://arxiv.org/pdf/2507.05914v1.pdf
| null |
[
"Rui Huang",
"Shitong Shao",
"Zikai Zhou",
"Pukun Zhao",
"Hangyu Guo",
"Tian Ye",
"Lichen Bai",
"Shuo Yang",
"Zeke Xie"
] |
[
"Dataset Condensation",
"GPU"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/geomag-a-vision-language-model-for-pixel
|
2507.05887
| null | null |
GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing
|
The application of Vision-Language Models (VLMs) in remote sensing (RS) image understanding has achieved notable progress, demonstrating the basic ability to recognize and describe geographical entities. However, existing RS-VLMs are mostly limited to image-level and region-level tasks, lacking the capability to handle pixel-level tasks and performing poorly in small-object recognition scenarios. Moreover, RS-VLMs consume significant computational resources when processing high-resolution RS images, further restricting their practical applicability. In this context, we propose GeoMag (Geographical Magnifier), an end-to-end general-purpose large model framework for RS. GeoMag dynamically focuses the attention scope based on prompt semantics to effectively perform remote sensing image parsing across multiple levels of granularity. This method introduces Task-driven Multi-granularity Resolution Adjustment (TMRA) and Prompt-guided Semantic-aware Cropping (PSC), which adaptively reduce the spatial resolution of task-irrelevant regions while enhancing the visual representation of task-relevant areas. This approach improves the model's perception of critical target regions, suppresses background redundancy, and reduces the computational cost of interpreting high-resolution RS imagery. Extensive comparative experiments on 10 benchmarks demonstrate that GeoMag not only excels in handling pixel-level tasks but also maintains competitive performance across tasks of other granularities compared to existing RS-VLMs.
| null |
https://arxiv.org/abs/2507.05887v1
|
https://arxiv.org/pdf/2507.05887v1.pdf
| null |
[
"Xianzhi Ma",
"Jianhui Li",
"Changhua Pei",
"Hao liu"
] |
[
"Language Modeling",
"Language Modelling",
"Object Recognition"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/usigan-unbalanced-self-information-feature
|
2507.05843
| null | null |
USIGAN: Unbalanced Self-Information Feature Transport for Weakly Paired Image IHC Virtual Staining
|
Immunohistochemical (IHC) virtual staining is a task that generates virtual IHC images from H\&E images while maintaining pathological semantic consistency with adjacent slices. This task aims to achieve cross-domain mapping between morphological structures and staining patterns through generative models, providing an efficient and cost-effective solution for pathological analysis. However, under weakly paired conditions, spatial heterogeneity between adjacent slices presents significant challenges. This can lead to inaccurate one-to-many mappings and generate results that are inconsistent with the pathological semantics of adjacent slices. To address this issue, we propose a novel unbalanced self-information feature transport for IHC virtual staining, named USIGAN, which extracts global morphological semantics without relying on positional correspondence.By removing weakly paired terms in the joint marginal distribution, we effectively mitigate the impact of weak pairing on joint distributions, thereby significantly improving the content consistency and pathological semantic consistency of the generated results. Moreover, we design the Unbalanced Optimal Transport Consistency (UOT-CTM) mechanism and the Pathology Self-Correspondence (PC-SCM) mechanism to construct correlation matrices between H\&E and generated IHC in image-level and real IHC and generated IHC image sets in intra-group level.. Experiments conducted on two publicly available datasets demonstrate that our method achieves superior performance across multiple clinically significant metrics, such as IoD and Pearson-R correlation, demonstrating better clinical relevance.
|
To address this issue, we propose a novel unbalanced self-information feature transport for IHC virtual staining, named USIGAN, which extracts global morphological semantics without relying on positional correspondence. By removing weakly paired terms in the joint marginal distribution, we effectively mitigate the impact of weak pairing on joint distributions, thereby significantly improving the content consistency and pathological semantic consistency of the generated results.
|
https://arxiv.org/abs/2507.05843v1
|
https://arxiv.org/pdf/2507.05843v1.pdf
| null |
[
"Yue Peng",
"Bing Xiong",
"Fuqiang Chen",
"De Eybo",
"Ranran Zhang",
"Wanming Hu",
"Jing Cai",
"Wenjian Qin"
] |
[
"Virtual Staining"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fast-and-accurate-collision-probability
|
2507.06149
| null | null |
Fast and Accurate Collision Probability Estimation for Autonomous Vehicles using Adaptive Sigma-Point Sampling
|
A novel algorithm is presented for the estimation of collision probabilities between dynamic objects with uncertain trajectories, where the trajectories are given as a sequence of poses with Gaussian distributions. We propose an adaptive sigma-point sampling scheme, which ultimately produces a fast, simple algorithm capable of estimating the collision probability with a median error of 3.5%, and a median runtime of 0.21ms, when measured on an Intel Xeon Gold 6226R Processor. Importantly, the algorithm explicitly accounts for the collision probability's temporal dependence, which is often neglected in prior work and otherwise leads to an overestimation of the collision probability. Finally, the method is tested on a diverse set of relevant real-world scenarios, consisting of 400 6-second snippets of autonomous vehicle logs, where the accuracy and latency is rigorously evaluated.
| null |
https://arxiv.org/abs/2507.06149v1
|
https://arxiv.org/pdf/2507.06149v1.pdf
| null |
[
"Charles Champagne Cossette",
"Taylor Scott Clawson",
"Andrew Feit"
] |
[
"Autonomous Vehicles"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/subspace-based-approximate-hessian-method-for
|
2507.06125
| null | null |
Subspace-based Approximate Hessian Method for Zeroth-Order Optimization
|
Zeroth-order optimization addresses problems where gradient information is inaccessible or impractical to compute. While most existing methods rely on first-order approximations, incorporating second-order (curvature) information can, in principle, significantly accelerate convergence. However, the high cost of function evaluations required to estimate Hessian matrices often limits practical applicability. We present the subspace-based approximate Hessian (ZO-SAH) method, a zeroth-order optimization algorithm that mitigates these costs by focusing on randomly selected two-dimensional subspaces. Within each subspace, ZO-SAH estimates the Hessian by fitting a quadratic polynomial to the objective function and extracting its second-order coefficients. To further reduce function-query costs, ZO-SAH employs a periodic subspace-switching strategy that reuses function evaluations across optimization steps. Experiments on eight benchmark datasets, including logistic regression and deep neural network training tasks, demonstrate that ZO-SAH achieves significantly faster convergence than existing zeroth-order methods.
| null |
https://arxiv.org/abs/2507.06125v1
|
https://arxiv.org/pdf/2507.06125v1.pdf
| null |
[
"Dongyoon Kim",
"Sungjae Lee",
"Wonjin Lee",
"Kwang In Kim"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Logistic Regression**, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function.\r\n\r\nSource: [scikit-learn](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)\r\n\r\nImage: [Michaelg2015](https://commons.wikimedia.org/wiki/User:Michaelg2015)",
"full_name": "Logistic Regression",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Generalized Linear Models (GLMs)** are a class of models that generalize upon linear regression by allowing many more distributions to be modeled for the response variable via a link function. Below you can find a continuously updating list of GLMs.",
"name": "Generalized Linear Models",
"parent": null
},
"name": "Logistic Regression",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/a-survey-on-prompt-tuning
|
2507.06085
| null | null |
A Survey on Prompt Tuning
|
This survey reviews prompt tuning, a parameter-efficient approach for adapting language models by prepending trainable continuous vectors while keeping the model frozen. We classify existing approaches into two categories: direct prompt learning and transfer learning. Direct prompt learning methods include: general optimization approaches, encoder-based methods, decomposition strategies, and mixture-of-experts frameworks. Transfer learning methods consist of: general transfer approaches, encoder-based methods, and decomposition strategies. For each method, we analyze method designs, innovations, insights, advantages, and disadvantages, with illustrative visualizations comparing different frameworks. We identify challenges in computational efficiency and training stability, and discuss future directions in improving training robustness and broadening application scope.
|
This survey reviews prompt tuning, a parameter-efficient approach for adapting language models by prepending trainable continuous vectors while keeping the model frozen.
|
https://arxiv.org/abs/2507.06085v2
|
https://arxiv.org/pdf/2507.06085v2.pdf
| null |
[
"Zongqian Li",
"Yixuan Su",
"Nigel Collier"
] |
[
"Computational Efficiency",
"Mixture-of-Experts",
"Prompt Learning",
"Survey",
"Transfer Learning"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ecore-energy-conscious-optimized-routing-for
|
2507.06011
| null | null |
ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge
|
Edge computing enables data processing closer to the source, significantly reducing latency an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environments. However, these tasks place substantial demands on resource constrained edge devices, making the joint optimization of energy consumption and detection accuracy critical. To address this challenge, we propose ECORE, a framework that integrates multiple dynamic routing strategies including estimation based techniques and a greedy selection algorithm to direct image processing requests to the most suitable edge device-model pair. ECORE dynamically balances energy efficiency and detection performance based on object characteristics. We evaluate our approach through extensive experiments on real-world datasets, comparing the proposed routers against widely used baseline techniques. The evaluation leverages established object detection models (YOLO, SSD, EfficientDet) and diverse edge platforms, including Jetson Orin Nano, Raspberry Pi 4 and 5, and TPU accelerators. Results demonstrate that our proposed context-aware routing strategies can reduce energy consumption and latency by 45% and 49%, respectively, while incurring only a 2% loss in detection accuracy compared to accuracy-centric methods.
| null |
https://arxiv.org/abs/2507.06011v1
|
https://arxiv.org/pdf/2507.06011v1.pdf
| null |
[
"Daghash K. Alqahtani",
"Maria A. Rodriguez",
"Muhammad Aamir Cheema",
"Hamid Rezatofighi",
"Adel N. Toosi"
] |
[
"Edge-computing",
"Object",
"object-detection",
"Object Detection",
"Raspberry Pi 4"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Non Maximum Suppression** is a computer vision method that selects a single entity out of many overlapping entities (for example bounding boxes in object detection). The criteria is usually discarding entities that are below a given probability bound. With remaining entities we repeatedly pick the entity with the highest probability, output that as the prediction, and discard any remaining box where a $\\text{IoU} \\geq 0.5$ with the box output in the previous step.\r\n\r\nImage Credit: [Martin Kersner](https://github.com/martinkersner/non-maximum-suppression-cpp)",
"full_name": "Non Maximum Suppression",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Proposal Filtering",
"parent": null
},
"name": "Non Maximum Suppression",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/amdegroot/ssd.pytorch/blob/5b0b77faa955c1917b0c710d770739ba8fbff9b7/ssd.py#L10",
"description": "**SSD** is a single-stage object detection method that discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. \r\n\r\nThe fundamental improvement in speed comes from eliminating bounding box proposals and the subsequent pixel or feature resampling stage. Improvements over competing single-stage methods include using a small convolutional filter to predict object categories and offsets in bounding box locations, using separate predictors (filters) for different aspect ratio detections, and applying these filters to multiple feature maps from the later stages of a network in order to perform detection at multiple scales.",
"full_name": "SSD",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Object Detection Models** are architectures used to perform the task of object detection. Below you can find a continuously updating list of object detection models.",
"name": "Object Detection Models",
"parent": null
},
"name": "SSD",
"source_title": "SSD: Single Shot MultiBox Detector",
"source_url": "http://arxiv.org/abs/1512.02325v5"
}
] |
https://paperswithcode.com/paper/remember-past-anticipate-future-learning
|
2507.05939
| null | null |
Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectors
|
Nowadays, misinformation articles, especially multimodal ones, are widely spread on social media platforms and cause serious negative effects. To control their propagation, Multimodal Misinformation Detection (MMD) becomes an active topic in the community to automatically identify misinformation. Previous MMD methods focus on supervising detectors by collecting offline data. However, in real-world scenarios, new events always continually emerge, making MMD models trained on offline data consistently outdated and ineffective. To address this issue, training MMD models under online data streams is an alternative, inducing an emerging task named continual MMD. Unfortunately, it is hindered by two major challenges. First, training on new data consistently decreases the detection performance on past data, named past knowledge forgetting. Second, the social environment constantly evolves over time, affecting the generalization on future data. To alleviate these challenges, we propose to remember past knowledge by isolating interference between event-specific parameters with a Dirichlet process-based mixture-of-expert structure, and anticipate future environmental distributions by learning a continuous-time dynamics model. Accordingly, we induce a new continual MMD method DAEDCMD. Extensive experiments demonstrate that DAEDCMD can consistently and significantly outperform the compared methods, including six MMD baselines and three continual learning methods.
|
First, training on new data consistently decreases the detection performance on past data, named past knowledge forgetting.
|
https://arxiv.org/abs/2507.05939v1
|
https://arxiv.org/pdf/2507.05939v1.pdf
| null |
[
"Bing Wang",
"Ximing Li",
"Mengzhe Ye",
"Changchun Li",
"Bo Fu",
"Jianfeng Qu",
"Lin Yuanbo Wu"
] |
[
"Articles",
"Continual Learning",
"Misinformation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/a-directed-lazy-random-walk-model-to-three
|
2507.06126
| null | null |
A Directed Lazy Random Walk Model to Three-Way Dynamic Matching Problem
|
This paper explores a novel extension of dynamic matching theory by analyzing a three-way matching problem involving agents from three distinct populations, each with two possible types. Unlike traditional static or two-way dynamic models, our setting captures more complex team-formation environments where one agent from each of the three populations must be matched to form a valid team. We consider two preference structures: assortative or homophilic, where agents prefer to be matched with others of the same type, and dis-assortative or heterophilic, where diversity within the team is valued. Agents arrive sequentially and face a trade-off between matching immediately or waiting for a higher quality match in the future albeit with a waiting cost. We construct and analyze the corresponding transition probability matrices for each preference regime and demonstrate the existence and uniqueness of stationary distributions. Our results show that stable and efficient outcomes can arise in dynamic, multi-agent matching environments, offering a deeper understanding of how complex matching processes evolve over time and how they can be effectively managed.
| null |
https://arxiv.org/abs/2507.06126v1
|
https://arxiv.org/pdf/2507.06126v1.pdf
| null |
[
"Souvik Roy",
"Agamani Saha"
] |
[
"valid"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ris-enabled-transmitter-design-for-joint
|
2507.06028
| null | null |
RIS-Enabled Transmitter Design for Joint Radar and Communication
|
Achieving efficient and cost-effective transmit beampattern control for integrated sensing and communication (ISAC) systems is a significant challenge. This paper addresses this by proposing a dual-function radar communication (DFRC) transmitter based on a reconfigurable intelligent surface (RIS) illuminated by a limited number of active sources. We formulate and solve the joint design of source waveforms and RIS phase shifts to match a desired space-frequency radiation pattern, and we evaluate the resulting ISAC system's performance in terms of radar detection probability and data transmission rate. Numerical results demonstrate the promising capabilities of this RIS-enabled transmitter for ISAC applications.
| null |
https://arxiv.org/abs/2507.06028v1
|
https://arxiv.org/pdf/2507.06028v1.pdf
| null |
[
"Emanuele Grossi",
"Marco Lops",
"Luca Venturino"
] |
[
"Integrated sensing and communication",
"ISAC"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/openfactscore-open-source-atomic-evaluation
|
2507.05965
| null | null |
OpenFActScore: Open-Source Atomic Evaluation of Factuality in Text Generation
|
We introduce OpenFActScore, an open-source implementation of the FActScore framework for evaluating the factuality of text generated by large language models (LLMs). FActScore evaluates the factual accuracy of long-form text by using Atomic Fact Generation (AFG) to extract individual factual claims and Atomic Fact Validation (AFV) to verify each claim against a trusted knowledge source. While the original FActScore relies on closed-source and commercial models such as InstructGPT and ChatGPT, OpenFActScore enables the use of any Hugging Face-compatible model for both AFG and AFV. We provide a detailed technical overview of our implementation, highlighting design choices and modifications made to support open models. We evaluate multiple open-source LLMs on both AFG and AFV using the original FActScore benchmark, reporting BERTScore-F1 for AFG and Error Rate relative to human annotations for AFV. Our results show that open models can approximate the performance of closed-source systems, with Gemma achieving the best overall performance, and our final setup obtains a 0.99 Pearson correlation with the original FActScore experiments. OpenFActScore promotes transparency, reproducibility, and cost-effective evaluation, and is available at: https://github.com/lflage/OpenFActScore.
|
We introduce OpenFActScore, an open-source implementation of the FActScore framework for evaluating the factuality of text generated by large language models (LLMs).
|
https://arxiv.org/abs/2507.05965v1
|
https://arxiv.org/pdf/2507.05965v1.pdf
| null |
[
"Lucas Fonseca Lage",
"Simon Ostermann"
] |
[
"Text Generation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/reflections-unlock-geometry-aware-reflection
|
2507.06103
| null | null |
Reflections Unlock: Geometry-Aware Reflection Disentanglement in 3D Gaussian Splatting for Photorealistic Scenes Rendering
|
Accurately rendering scenes with reflective surfaces remains a significant challenge in novel view synthesis, as existing methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) often misinterpret reflections as physical geometry, resulting in degraded reconstructions. Previous methods rely on incomplete and non-generalizable geometric constraints, leading to misalignment between the positions of Gaussian splats and the actual scene geometry. When dealing with real-world scenes containing complex geometry, the accumulation of Gaussians further exacerbates surface artifacts and results in blurred reconstructions. To address these limitations, in this work, we propose Ref-Unlock, a novel geometry-aware reflection modeling framework based on 3D Gaussian Splatting, which explicitly disentangles transmitted and reflected components to better capture complex reflections and enhance geometric consistency in real-world scenes. Our approach employs a dual-branch representation with high-order spherical harmonics to capture high-frequency reflective details, alongside a reflection removal module providing pseudo reflection-free supervision to guide clean decomposition. Additionally, we incorporate pseudo-depth maps and a geometry-aware bilateral smoothness constraint to enhance 3D geometric consistency and stability in decomposition. Extensive experiments demonstrate that Ref-Unlock significantly outperforms classical GS-based reflection methods and achieves competitive results with NeRF-based models, while enabling flexible vision foundation models (VFMs) driven reflection editing. Our method thus offers an efficient and generalizable solution for realistic rendering of reflective scenes. Our code is available at https://ref-unlock.github.io/.
| null |
https://arxiv.org/abs/2507.06103v1
|
https://arxiv.org/pdf/2507.06103v1.pdf
| null |
[
"Jiayi Song",
"Zihan Ye",
"Qingyuan Zhou",
"Weidong Yang",
"Ben Fei",
"Jingyi Xu",
"Ying He",
"Wanli Ouyang"
] |
[
"3DGS",
"Disentanglement",
"NeRF",
"Novel View Synthesis",
"Reflection Removal"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/discontinuity-aware-normal-integration-for
|
2507.06075
| null | null |
Discontinuity-aware Normal Integration for Generic Central Camera Models
|
Recovering a 3D surface from its surface normal map, a problem known as normal integration, is a key component for photometric shape reconstruction techniques such as shape-from-shading and photometric stereo. The vast majority of existing approaches for normal integration handle only implicitly the presence of depth discontinuities and are limited to orthographic or ideal pinhole cameras. In this paper, we propose a novel formulation that allows modeling discontinuities explicitly and handling generic central cameras. Our key idea is based on a local planarity assumption, that we model through constraints between surface normals and ray directions. Compared to existing methods, our approach more accurately approximates the relation between depth and surface normals, achieves state-of-the-art results on the standard normal integration benchmark, and is the first to directly handle generic central camera models.
| null |
https://arxiv.org/abs/2507.06075v1
|
https://arxiv.org/pdf/2507.06075v1.pdf
| null |
[
"Francesco Milano",
"Manuel López-Antequera",
"Naina Dhingra",
"Roland Siegwart",
"Robert Thiel"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/visualspeaker-visually-guided-3d-avatar-lip
|
2507.06060
| null | null |
VisualSpeaker: Visually-Guided 3D Avatar Lip Synthesis
|
Realistic, high-fidelity 3D facial animations are crucial for expressive avatar systems in human-computer interaction and accessibility. Although prior methods show promising quality, their reliance on the mesh domain limits their ability to fully leverage the rapid visual innovations seen in 2D computer vision and graphics. We propose VisualSpeaker, a novel method that bridges this gap using photorealistic differentiable rendering, supervised by visual speech recognition, for improved 3D facial animation. Our contribution is a perceptual lip-reading loss, derived by passing photorealistic 3D Gaussian Splatting avatar renders through a pre-trained Visual Automatic Speech Recognition model during training. Evaluation on the MEAD dataset demonstrates that VisualSpeaker improves both the standard Lip Vertex Error metric by 56.1% and the perceptual quality of the generated animations, while retaining the controllability of mesh-driven animation. This perceptual focus naturally supports accurate mouthings, essential cues that disambiguate similar manual signs in sign language avatars.
| null |
https://arxiv.org/abs/2507.06060v1
|
https://arxiv.org/pdf/2507.06060v1.pdf
| null |
[
"Alexandre Symeonidis-Herzig",
"Özge Mercanoğlu Sincan",
"Richard Bowden"
] |
[
"Automatic Speech Recognition",
"Lip Reading",
"speech-recognition",
"Speech Recognition",
"Visual Speech Recognition"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/hidden-prompts-in-manuscripts-exploit-ai
|
2507.06185
| null | null |
Hidden Prompts in Manuscripts Exploit AI-Assisted Peer Review
|
In July 2025, 18 academic manuscripts on the preprint website arXiv were found to contain hidden instructions known as prompts designed to manipulate AI-assisted peer review. Instructions such as "GIVE A POSITIVE REVIEW ONLY" were concealed using techniques like white-colored text. Author responses varied: one planned to withdraw the affected paper, while another defended the practice as legitimate testing of reviewer compliance. This commentary analyzes this practice as a novel form of research misconduct. We examine the technique of prompt injection in large language models (LLMs), revealing four types of hidden prompts, ranging from simple positive review commands to detailed evaluation frameworks. The defense that prompts served as "honeypots" to detect reviewers improperly using AI fails under examination--the consistently self-serving nature of prompt instructions indicates intent to manipulate. Publishers maintain inconsistent policies: Elsevier prohibits AI use in peer review entirely, while Springer Nature permits limited use with disclosure requirements. The incident exposes systematic vulnerabilities extending beyond peer review to any automated system processing scholarly texts, including plagiarism detection and citation indexing. Our analysis underscores the need for coordinated technical screening at submission portals and harmonized policies governing generative AI (GenAI) use in academic evaluation.
| null |
https://arxiv.org/abs/2507.06185v1
|
https://arxiv.org/pdf/2507.06185v1.pdf
| null |
[
"Zhicheng Lin"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/openagentsafety-a-comprehensive-framework-for
|
2507.06134
| null | null |
OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety
|
Recent advances in AI agents capable of solving complex, everyday tasks, from scheduling to customer service, have enabled deployment in real-world settings, but their possibilities for unsafe behavior demands rigorous evaluation. While prior benchmarks have attempted to assess agent safety, most fall short by relying on simulated environments, narrow task domains, or unrealistic tool abstractions. We introduce OpenAgentSafety, a comprehensive and modular framework for evaluating agent behavior across eight critical risk categories. Unlike prior work, our framework evaluates agents that interact with real tools, including web browsers, code execution environments, file systems, bash shells, and messaging platforms; and supports over 350 multi-turn, multi-user tasks spanning both benign and adversarial user intents. OpenAgentSafety is designed for extensibility, allowing researchers to add tools, tasks, websites, and adversarial strategies with minimal effort. It combines rule-based analysis with LLM-as-judge assessments to detect both overt and subtle unsafe behaviors. Empirical analysis of five prominent LLMs in agentic scenarios reveals unsafe behavior in 51.2% of safety-vulnerable tasks with Claude-Sonnet-3.7, to 72.7% with o3-mini, highlighting critical safety vulnerabilities and the need for stronger safeguards before real-world deployment.
| null |
https://arxiv.org/abs/2507.06134v1
|
https://arxiv.org/pdf/2507.06134v1.pdf
| null |
[
"Sanidhya Vijayvargiya",
"Aditya Bharat Soni",
"Xuhui Zhou",
"Zora Zhiruo Wang",
"Nouha Dziri",
"Graham Neubig",
"Maarten Sap"
] |
[
"AI Agent",
"Scheduling"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/safe-domain-randomization-via-uncertainty
|
2507.06111
| null | null |
Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation
|
Deploying reinforcement learning (RL) policies in real-world involves significant challenges, including distribution shifts, safety concerns, and the impracticality of direct interactions during policy refinement. Existing methods, such as domain randomization (DR) and off-dynamics RL, enhance policy robustness by direct interaction with the target domain, an inherently unsafe practice. We propose Uncertainty-Aware RL (UARL), a novel framework that prioritizes safety during training by addressing Out-Of-Distribution (OOD) detection and policy adaptation without requiring direct interactions in target domain. UARL employs an ensemble of critics to quantify policy uncertainty and incorporates progressive environmental randomization to prepare the policy for diverse real-world conditions. By iteratively refining over high-uncertainty regions of the state space in simulated environments, UARL enhances robust generalization to the target domain without explicitly training on it. We evaluate UARL on MuJoCo benchmarks and a quadrupedal robot, demonstrating its effectiveness in reliable OOD detection, improved performance, and enhanced sample efficiency compared to baselines.
| null |
https://arxiv.org/abs/2507.06111v1
|
https://arxiv.org/pdf/2507.06111v1.pdf
| null |
[
"Mohamad H. Danesh",
"Maxime Wabartha",
"Stanley Wu",
"Joelle Pineau",
"Hsiu-Chin Lin"
] |
[
"MuJoCo",
"Out-of-Distribution Detection",
"Out of Distribution (OOD) Detection",
"Reinforcement Learning (RL)"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/few-shot-learning-by-explicit-physics
|
2507.06062
| null | null |
Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
|
Machine learning methods often struggle with real-world applications in science and engineering due to limited or low-quality training data. In this work, the example of groundwater flow with heat transport is considered; this corresponds to an advection-diffusion process under heterogeneous flow conditions, that is, spatially distributed material parameters and heat sources. Classical numerical simulations are costly and challenging due to high spatio-temporal resolution requirements and large domains. While often computationally more efficient, purely data-driven surrogate models face difficulties, particularly in predicting the advection process, which is highly sensitive to input variations and involves long-range spatial interactions. Therefore, in this work, a Local-Global Convolutional Neural Network (LGCNN) approach is introduced. It combines a lightweight numerical surrogate for the transport process (global) with convolutional neural networks for the groundwater velocity and heat diffusion processes (local). With the LGCNN, a city-wide subsurface temperature field is modeled, involving a heterogeneous groundwater flow field and one hundred groundwater heat pump injection points forming interacting heat plumes over long distances. The model is first systematically analyzed based on random subsurface input fields. Then, the model is trained on a handful of cut-outs from a real-world subsurface map of the Munich region in Germany, and it scales to larger cut-outs without retraining. All datasets, our code, and trained models are published for reproducibility.
|
It combines a lightweight numerical surrogate for the transport process (global) with convolutional neural networks for the groundwater velocity and heat diffusion processes (local).
|
https://arxiv.org/abs/2507.06062v1
|
https://arxiv.org/pdf/2507.06062v1.pdf
| null |
[
"Julia Pelzer",
"Corné Verburg",
"Alexander Heinlein",
"Miriam Schulte"
] |
[
"Few-Shot Learning"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/fredholm-neural-networks-for-forward-and
|
2507.06038
| null | null |
Fredholm Neural Networks for forward and inverse problems in elliptic PDEs
|
Building on our previous work introducing Fredholm Neural Networks (Fredholm NNs/ FNNs) for solving integral equations, we extend the framework to tackle forward and inverse problems for linear and semi-linear elliptic partial differential equations. The proposed scheme consists of a deep neural network (DNN) which is designed to represent the iterative process of fixed-point iterations for the solution of elliptic PDEs using the boundary integral method within the framework of potential theory. The number of layers, weights, biases and hyperparameters are computed in an explainable manner based on the iterative scheme, and we therefore refer to this as the Potential Fredholm Neural Network (PFNN). We show that this approach ensures both accuracy and explainability, achieving small errors in the interior of the domain, and near machine-precision on the boundary. We provide a constructive proof for the consistency of the scheme and provide explicit error bounds for both the interior and boundary of the domain, reflected in the layers of the PFNN. These error bounds depend on the approximation of the boundary function and the integral discretization scheme, both of which directly correspond to components of the Fredholm NN architecture. In this way, we provide an explainable scheme that explicitly respects the boundary conditions. We assess the performance of the proposed scheme for the solution of both the forward and inverse problem for linear and semi-linear elliptic PDEs in two dimensions.
| null |
https://arxiv.org/abs/2507.06038v2
|
https://arxiv.org/pdf/2507.06038v2.pdf
| null |
[
"Kyriakos Georgiou",
"Constantinos Siettos",
"Athanasios N. Yannacopoulos"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-survey-on-latent-reasoning
|
2507.06203
| null | null |
A Survey on Latent Reasoning
|
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, especially when guided by explicit chain-of-thought (CoT) reasoning that verbalizes intermediate steps. While CoT improves both interpretability and accuracy, its dependence on natural language reasoning limits the model's expressive bandwidth. Latent reasoning tackles this bottleneck by performing multi-step inference entirely in the model's continuous hidden state, eliminating token-level supervision. To advance latent reasoning research, this survey provides a comprehensive overview of the emerging field of latent reasoning. We begin by examining the foundational role of neural network layers as the computational substrate for reasoning, highlighting how hierarchical representations support complex transformations. Next, we explore diverse latent reasoning methodologies, including activation-based recurrence, hidden state propagation, and fine-tuning strategies that compress or internalize explicit reasoning traces. Finally, we discuss advanced paradigms such as infinite-depth latent reasoning via masked diffusion models, which enable globally consistent and reversible reasoning processes. By unifying these perspectives, we aim to clarify the conceptual landscape of latent reasoning and chart future directions for research at the frontier of LLM cognition. An associated GitHub repository collecting the latest papers and repos is available at: https://github.com/multimodal-art-projection/LatentCoT-Horizon/.
|
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, especially when guided by explicit chain-of-thought (CoT) reasoning that verbalizes intermediate steps.
|
https://arxiv.org/abs/2507.06203v2
|
https://arxiv.org/pdf/2507.06203v2.pdf
| null |
[
"Rui-Jie Zhu",
"Tianhao Peng",
"Tianhao Cheng",
"Xingwei Qu",
"Jinfa Huang",
"Dawei Zhu",
"Hao Wang",
"Kaiwen Xue",
"Xuanliang Zhang",
"Yong Shan",
"Tianle Cai",
"Taylor Kergan",
"Assel Kembay",
"Andrew Smith",
"Chenghua Lin",
"Binh Nguyen",
"Yuqi Pan",
"Yuhong Chou",
"Zefan Cai",
"Zhenhe Wu",
"Yongchi Zhao",
"Tianyu Liu",
"Jian Yang",
"Wangchunshu Zhou",
"Chujie Zheng",
"Chongxuan Li",
"Yuyin Zhou",
"Zhoujun Li",
"Zhaoxiang Zhang",
"Jiaheng Liu",
"Ge Zhang",
"Wenhao Huang",
"Jason Eshraghian"
] |
[
"Survey"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/ai-based-environment-aware-xl-mimo-channel
|
2507.06066
| null | null |
AI-based Environment-Aware XL-MIMO Channel Estimation with Location-Specific Prior Knowledge Enabled by CKM
|
Accurate and efficient acquisition of wireless channel state information (CSI) is crucial to enhance the communication performance of wireless systems. However, with the continuous densification of wireless links, increased channel dimensions, and the use of higher-frequency bands, channel estimation in the sixth generation (6G) and beyond wireless networks faces new challenges, such as insufficient orthogonal pilot sequences, inadequate signal-to-noise ratio (SNR) for channel training, and more sophisticated channel statistical distributions in complex environment. These challenges pose significant difficulties for classical channel estimation algorithms like least squares (LS) and maximum a posteriori (MAP). To address this problem, we propose a novel environment-aware channel estimation framework with location-specific prior channel distribution enabled by the new concept of channel knowledge map (CKM). To this end, we propose a new type of CKM called channel score function map (CSFM), which learns the channel probability density function (PDF) using artificial intelligence (AI) techniques. To fully exploit the prior information in CSFM, we propose a plug-and-play (PnP) based algorithm to decouple the regularized MAP channel estimation problem, thereby reducing the complexity of the optimization process. Besides, we employ Tweedie's formula to establish a connection between the channel score function, defined as the logarithmic gradient of the channel PDF, and the channel denoiser. This allows the use of the high-precision, environment-aware channel denoiser from the CSFM to approximate the channel score function, thus enabling efficient processing of the decoupled channel statistical components. Simulation results show that the proposed CSFM-PnP based channel estimation technique significantly outperforms the conventional techniques in the aforementioned challenging scenarios.
| null |
https://arxiv.org/abs/2507.06066v1
|
https://arxiv.org/pdf/2507.06066v1.pdf
| null |
[
"Yuelong Qiu",
"Di wu",
"Yong Zeng",
"Yanqun Tang",
"Nan Cheng",
"Chenhao Qi"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/minimal-deterministic-echo-state-networks
|
2507.06050
| null | null |
Minimal Deterministic Echo State Networks Outperform Random Reservoirs in Learning Chaotic Dynamics
|
Machine learning (ML) is widely used to model chaotic systems. Among ML approaches, echo state networks (ESNs) have received considerable attention due to their simple construction and fast training. However, ESN performance is highly sensitive to hyperparameter choices and to its random initialization. In this work, we demonstrate that ESNs constructed using deterministic rules and simple topologies (MESNs) outperform standard ESNs in the task of chaotic attractor reconstruction. We use a dataset of more than 90 chaotic systems to benchmark 10 different minimal deterministic reservoir initializations. We find that MESNs obtain up to a 41% reduction in error compared to standard ESNs. Furthermore, we show that the MESNs are more robust, exhibiting less inter-run variation, and have the ability to reuse hyperparameters across different systems. Our results illustrate how structured simplicity in ESN design can outperform stochastic complexity in learning chaotic dynamics.
| null |
https://arxiv.org/abs/2507.06050v1
|
https://arxiv.org/pdf/2507.06050v1.pdf
| null |
[
"Francesco Martinuzzi"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/secure-communication-of-uav-mounted-star-ris
|
2507.06048
| null | null |
Secure Communication of UAV-mounted STAR-RIS under Phase Shift Errors
|
This paper investigates the secure communication capabilities of a non-orthogonal multiple access (NOMA) network supported by a STAR-RIS (simultaneously transmitting and reflecting reconfigurable intelligent surface) deployed on an unmanned aerial vehicle (UAV), in the presence of passive eavesdroppers. The STAR-RIS facilitates concurrent signal reflection and transmission, allowing multiple legitimate users-grouped via NOMA-to be served efficiently, thereby improving spectral utilization. Each user contends with an associated eavesdropper, creating a stringent security scenario. Under Nakagami fading conditions and accounting for phase shift inaccuracies in the STAR-RIS, closed-form expressions for the ergodic secrecy rates of users in both transmission and reflection paths are derived. An optimization framework is then developed to jointly adjust the UAV's positioning and the STAR-RIS power splitting coefficient, aiming to maximize the system's secrecy rate. The proposed approach enhances secure transmission in STAR-RIS-NOMA configurations under realistic hardware constraints and offers valuable guidance for the design of future 6G wireless networks.
| null |
https://arxiv.org/abs/2507.06048v1
|
https://arxiv.org/pdf/2507.06048v1.pdf
| null |
[
"Aseel Qsibat",
"Habiba Akhleifa",
"Abdelhamid Salem",
"Khaled Rabie",
"Xingwang Li",
"Thokozani Shongwe",
"Mohamad A. Alawad",
"Yazeed Alkhrijah"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/cavgan-unifying-jailbreak-and-defense-of-llms
|
2507.06043
| null | null |
CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations
|
Security alignment enables the Large Language Model (LLM) to gain the protection against malicious queries, but various jailbreak attack methods reveal the vulnerability of this security mechanism. Previous studies have isolated LLM jailbreak attacks and defenses. We analyze the security protection mechanism of the LLM, and propose a framework that combines attack and defense. Our method is based on the linearly separable property of LLM intermediate layer embedding, as well as the essence of jailbreak attack, which aims to embed harmful problems and transfer them to the safe area. We utilize generative adversarial network (GAN) to learn the security judgment boundary inside the LLM to achieve efficient jailbreak attack and defense. The experimental results indicate that our method achieves an average jailbreak success rate of 88.85\% across three popular LLMs, while the defense success rate on the state-of-the-art jailbreak dataset reaches an average of 84.17\%. This not only validates the effectiveness of our approach but also sheds light on the internal security mechanisms of LLMs, offering new insights for enhancing model security The code and data are available at https://github.com/NLPGM/CAVGAN.
| null |
https://arxiv.org/abs/2507.06043v1
|
https://arxiv.org/pdf/2507.06043v1.pdf
| null |
[
"Xiaohu Li",
"Yunfeng Ning",
"Zepeng Bao",
"Mayi Xu",
"Jianhao Chen",
"Tieyun Qian"
] |
[
"Generative Adversarial Network",
"Large Language Model",
"LLM Jailbreak"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/edgecodec-onboard-lightweight-high-fidelity
|
2507.06040
| null | null |
EdgeCodec: Onboard Lightweight High Fidelity Neural Compressor with Residual Vector Quantization
|
We present EdgeCodec, an end-to-end neural compressor for barometric data collected from wind turbine blades. EdgeCodec leverages a heavily asymmetric autoencoder architecture, trained with a discriminator and enhanced by a Residual Vector Quantizer to maximize compression efficiency. It achieves compression rates between 2'560:1 and 10'240:1 while maintaining a reconstruction error below 3%, and operates in real time on the GAP9 microcontroller with bitrates ranging from 11.25 to 45 bits per second. Bitrates can be selected on a sample-by-sample basis, enabling on-the-fly adaptation to varying network conditions. In its highest compression mode, EdgeCodec reduces the energy consumption of wireless data transmission by up to 2.9x, significantly extending the operational lifetime of deployed sensor units.
|
We present EdgeCodec, an end-to-end neural compressor for barometric data collected from wind turbine blades.
|
https://arxiv.org/abs/2507.06040v1
|
https://arxiv.org/pdf/2507.06040v1.pdf
| null |
[
"Benjamin Hodo",
"Tommaso Polonelli",
"Amirhossein Moallemi",
"Luca Benini",
"Michele Magno"
] |
[
"Quantization"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/evaluation-of-habitat-robotics-using-large
|
2507.06157
| null | null |
Evaluation of Habitat Robotics using Large Language Models
|
This paper focuses on evaluating the effectiveness of Large Language Models at solving embodied robotic tasks using the Meta PARTNER benchmark. Meta PARTNR provides simplified environments and robotic interactions within randomized indoor kitchen scenes. Each randomized kitchen scene is given a task where two robotic agents cooperatively work together to solve the task. We evaluated multiple frontier models on Meta PARTNER environments. Our results indicate that reasoning models like OpenAI o3-mini outperform non-reasoning models like OpenAI GPT-4o and Llama 3 when operating in PARTNR's robotic embodied environments. o3-mini displayed outperform across centralized, decentralized, full observability, and partial observability configurations. This provides a promising avenue of research for embodied robotic development.
| null |
https://arxiv.org/abs/2507.06157v1
|
https://arxiv.org/pdf/2507.06157v1.pdf
| null |
[
"William Li",
"Lei Hamilton",
"Kaise Al-natour",
"Sanjeev Mohindra"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**LLaMA** is a collection of foundation language models ranging from 7B to 65B parameters. It is based on the transformer architecture with various improvements that were subsequently proposed. The main difference with the original architecture are listed below.\r\n\r\n- RMSNorm normalizing function is used to improve the training stability, by normalizing the input of each transformer sub-layer, instead of normalizing the output.\r\n- The ReLU non-linearity is replaced by the SwiGLU activation function to improve performance.\r\n- Absolute positional embeddings are removed and instead rotary positional embeddings (RoPE) are added at each layer of the network.",
"full_name": "LLaMA",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Language Models** are models for predicting the next word or character in a document. Below you can find a continuously updating list of language models.\r\n\r\n",
"name": "Language Models",
"parent": null
},
"name": "LLaMA",
"source_title": "LLaMA: Open and Efficient Foundation Language Models",
"source_url": "https://arxiv.org/abs/2302.13971v1"
}
] |
https://paperswithcode.com/paper/mp-aloe-an-r2scan-dataset-for-universal
|
2507.05559
| null | null |
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
|
We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created using active learning and primarily consists of off-equilibrium structures. We benchmark a machine learning interatomic potential trained on MP-ALOE, and evaluate its performance on a series of benchmarks, including predicting the thermochemical properties of equilibrium structures; predicting forces of far-from-equilibrium structures; maintaining physical soundness under static extreme deformations; and molecular dynamic stability under extreme temperatures and pressures. MP-ALOE shows strong performance on all of these benchmarks, and is made public for the broader community to utilize.
| null |
https://arxiv.org/abs/2507.05559v1
|
https://arxiv.org/pdf/2507.05559v1.pdf
| null |
[
"Matthew C. Kuner",
"Aaron D. Kaplan",
"Kristin A. Persson",
"Mark Asta",
"Daryl C. Chrzan"
] |
[
"Active Learning"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/estimating-interventional-distributions-with
|
2507.05526
| null | null |
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
|
In scientific domains -- from biology to the social sciences -- many questions boil down to \textit{What effect will we observe if we intervene on a particular variable?} If the causal relationships (e.g.~a causal graph) are known, it is possible to estimate the intervention distributions. In the absence of this domain knowledge, the causal structure must be discovered from the available observational data. However, observational data are often compatible with multiple causal graphs, making methods that commit to a single structure prone to overconfidence. A principled way to manage this structural uncertainty is via Bayesian inference, which averages over a posterior distribution on possible causal structures and functional mechanisms. Unfortunately, the number of causal structures grows super-exponentially with the number of nodes in the graph, making computations intractable. We propose to circumvent these challenges by using meta-learning to create an end-to-end model: the Model-Averaged Causal Estimation Transformer Neural Process (MACE-TNP). The model is trained to predict the Bayesian model-averaged interventional posterior distribution, and its end-to-end nature bypasses the need for expensive calculations. Empirically, we demonstrate that MACE-TNP outperforms strong Bayesian baselines. Our work establishes meta-learning as a flexible and scalable paradigm for approximating complex Bayesian causal inference, that can be scaled to increasingly challenging settings in the future.
| null |
https://arxiv.org/abs/2507.05526v1
|
https://arxiv.org/pdf/2507.05526v1.pdf
| null |
[
"Anish Dhir",
"Cristiana Diaconu",
"Valentinian Mihai Lungu",
"James Requeima",
"Richard E. Turner",
"Mark van der Wilk"
] |
[
"Bayesian Inference",
"Causal Inference",
"Meta-Learning"
] | 2025-07-07T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
}
] |
https://paperswithcode.com/paper/inaugural-moasei-competition-at-aamas-2025-a
|
2507.05469
| null | null |
Inaugural MOASEI Competition at AAMAS'2025: A Technical Report
|
We present the Methods for Open Agent Systems Evaluation Initiative (MOASEI) Competition, a multi-agent AI benchmarking event designed to evaluate decision-making under open-world conditions. Built on the free-range-zoo environment suite, MOASEI introduced dynamic, partially observable domains with agent and task openness--settings where entities may appear, disappear, or change behavior over time. The 2025 competition featured three tracks--Wildfire, Rideshare, and Cybersecurity--each highlighting distinct dimensions of openness and coordination complexity. Eleven teams from international institutions participated, with four of those teams submitting diverse solutions including graph neural networks, convolutional architectures, predictive modeling, and large language model--driven meta--optimization. Evaluation metrics centered on expected utility, robustness to perturbations, and responsiveness to environmental change. The results reveal promising strategies for generalization and adaptation in open environments, offering both empirical insight and infrastructure for future research. This report details the competition's design, findings, and contributions to the open-agent systems research community.
| null |
https://arxiv.org/abs/2507.05469v1
|
https://arxiv.org/pdf/2507.05469v1.pdf
| null |
[
"Ceferino Patino",
"Tyler J. Billings",
"Alireza Saleh Abadi",
"Daniel Redder",
"Adam Eck",
"Prashant Doshi",
"Leen-Kiat Soh"
] |
[
"Benchmarking",
"Decision Making",
"Language Modeling",
"Language Modelling",
"Large Language Model"
] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/infrastructuring-contestability-a-framework
|
2507.05187
| null | null |
Infrastructuring Contestability: A Framework for Community-Defined AI Value Pluralism
|
The proliferation of AI-driven systems presents a fundamental challenge to Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW), often diminishing user agency and failing to account for value pluralism. Current approaches to value alignment, which rely on centralized, top-down definitions, lack the mechanisms for meaningful contestability. This leaves users and communities unable to challenge or shape the values embedded in the systems that govern their digital lives, creating a crisis of legitimacy and trust. This paper introduces Community-Defined AI Value Pluralism (CDAVP), a socio-technical framework that addresses this gap. It reframes the design problem from achieving a single aligned state to infrastructuring a dynamic ecosystem for value deliberation and application. At its core, CDAVP enables diverse, self-organizing communities to define and maintain explicit value profiles - rich, machine-readable representations that can encompass not only preferences but also community-specific rights and duties. These profiles are then contextually activated by the end-user, who retains ultimate control (agency) over which values guide the AI's behavior. AI applications, in turn, are designed to transparently interpret these profiles and moderate conflicts, adhering to a set of non-negotiable, democratically-legitimated meta-rules. The designer's role shifts from crafting static interfaces to becoming an architect of participatory ecosystems. We argue that infrastructuring for pluralism is a necessary pathway toward achieving robust algorithmic accountability and genuinely contestable, human-centric AI.
| null |
https://arxiv.org/abs/2507.05187v1
|
https://arxiv.org/pdf/2507.05187v1.pdf
| null |
[
"Andreas Mayer"
] |
[] | 2025-07-07T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/ai-generated-text-detection-using-instruction
|
2507.05157
| null | null |
AI Generated Text Detection Using Instruction Fine-tuned Large Language and Transformer-Based Models
|
Large Language Models (LLMs) possess an extraordinary capability to produce text that is not only coherent and contextually relevant but also strikingly similar to human writing. They adapt to various styles and genres, producing content that is both grammatically correct and semantically meaningful. Recently, LLMs have been misused to create highly realistic phishing emails, spread fake news, generate code to automate cyber crime, and write fraudulent scientific articles. Additionally, in many real-world applications, the generated content including style and topic and the generator model are not known beforehand. The increasing prevalence and sophistication of artificial intelligence (AI)-generated texts have made their detection progressively more challenging. Various attempts have been made to distinguish machine-generated text from human-authored content using linguistic, statistical, machine learning, and ensemble-based approaches. This work focuses on two primary objectives Task-A, which involves distinguishing human-written text from machine-generated text, and Task-B, which attempts to identify the specific LLM model responsible for the generation. Both of these tasks are based on fine tuning of Generative Pre-trained Transformer (GPT_4o-mini), Large Language Model Meta AI (LLaMA) 3 8B, and Bidirectional Encoder Representations from Transformers (BERT). The fine-tuned version of GPT_4o-mini and the BERT model has achieved accuracies of 0.9547 for Task-A and 0.4698 for Task-B.
| null |
https://arxiv.org/abs/2507.05157v1
|
https://arxiv.org/pdf/2507.05157v1.pdf
| null |
[
"Chinnappa Guggilla",
"Budhaditya Roy",
"Trupti Ramdas Chavan",
"Abdul Rahman",
"Edward Bowen"
] |
[
"Articles",
"Large Language Model",
"Text Detection"
] | 2025-07-07T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Linear Warmup With Linear Decay** is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.",
"full_name": "Linear Warmup With Linear Decay",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Learning Rate Schedules** refer to schedules for the learning rate during the training of neural networks. Below you can find a continuously updating list of learning rate schedules.",
"name": "Learning Rate Schedules",
"parent": null
},
"name": "Linear Warmup With Linear Decay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "“How do I get a full refund from Expedia?\r\nHow do I get a full refund from Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!\r\n\r\n\r\n“How do I get a full refund from Expedia?\r\nHow do I get a full refund from Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!",
"full_name": "Refunds@Expedia|||How do I get a full refund from Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Refunds@Expedia|||How do I get a full refund from Expedia?",
"source_title": "Gaussian Error Linear Units (GELUs)",
"source_url": "https://arxiv.org/abs/1606.08415v5"
},
{
"code_snippet_url": "https://github.com/huggingface/transformers/blob/4dc65591b5c61d75c3ef3a2a883bf1433e08fc45/src/transformers/modeling_tf_bert.py#L271",
"description": "**Attention Dropout** is a type of [dropout](https://paperswithcode.com/method/dropout) used in attention-based architectures, where elements are randomly dropped out of the [softmax](https://paperswithcode.com/method/softmax) in the attention equation. For example, for scaled-dot product attention, we would drop elements from the first term:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$",
"full_name": "Attention Dropout",
"introduced_year": 2018,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Attention Dropout",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/google-research/bert",
"description": "**BERT**, or Bidirectional Encoder Representations from Transformers, improves upon standard [Transformers](http://paperswithcode.com/method/transformer) by removing the unidirectionality constraint by using a *masked language model* (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a *next sentence prediction* task that jointly pre-trains text-pair representations. \r\n\r\nThere are two steps in BERT: *pre-training* and *fine-tuning*. During pre-training, the model is trained on unlabeled data over different pre-training tasks. For fine-tuning, the BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks. Each downstream task has separate fine-tuned models, even though they\r\nare initialized with the same pre-trained parameters.",
"full_name": "BERT",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Language Models** are models for predicting the next word or character in a document. Below you can find a continuously updating list of language models.\r\n\r\n",
"name": "Language Models",
"parent": null
},
"name": "BERT",
"source_title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
"source_url": "https://arxiv.org/abs/1810.04805v2"
}
] |
https://paperswithcode.com/paper/meta-learning-transformers-to-improve-in
|
2507.05019
| null | null |
Meta-Learning Transformers to Improve In-Context Generalization
|
In-context learning enables transformer models to generalize to new tasks based solely on input prompts, without any need for weight updates. However, existing training paradigms typically rely on large, unstructured datasets that are costly to store, difficult to evaluate for quality and balance, and pose privacy and ethical concerns due to the inclusion of sensitive information. Motivated by these limitations and risks, we propose an alternative training strategy where we leverage a collection of multiple, small-scale, and domain-specific datasets. We empirically demonstrate that the increased quality and diversity of such data improve the generalization abilities of in-context learners beyond their training domain, while achieving comparable performance with models trained on a single large-scale dataset. We investigate this paradigm by leveraging meta-learning to train an in-context learner on the Meta-Album collection under several settings. Firstly, we show the performance in a controlled environment, where the test domain is completely excluded from the training knowledge. Secondly, we explore the robustness of these models to forgetting in a continual scenario where the information is accessible for a limited time. Finally, we explore the more challenging unsupervised scenario. Our findings demonstrate that transformers still generalize for in-context prediction when trained on a curated dataset collection while offering advantages in modularity and replaceability.
| null |
https://arxiv.org/abs/2507.05019v1
|
https://arxiv.org/pdf/2507.05019v1.pdf
| null |
[
"Lorenzo Braccaioli",
"Anna Vettoruzzo",
"Prabhant Singh",
"Joaquin Vanschoren",
"Mohamed-Rafik Bouguelia",
"Nicola Conci"
] |
[
"In-Context Learning",
"Meta-Learning"
] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/an-analysis-of-vision-language-models-for
|
2507.04735
| null | null |
An analysis of vision-language models for fabric retrieval
|
Effective cross-modal retrieval is essential for applications like information retrieval and recommendation systems, particularly in specialized domains such as manufacturing, where product information often consists of visual samples paired with a textual description. This paper investigates the use of Vision Language Models(VLMs) for zero-shot text-to-image retrieval on fabric samples. We address the lack of publicly available datasets by introducing an automated annotation pipeline that uses Multimodal Large Language Models (MLLMs) to generate two types of textual descriptions: freeform natural language and structured attribute-based descriptions. We produce these descriptions to evaluate retrieval performance across three Vision-Language Models: CLIP, LAION-CLIP, and Meta's Perception Encoder. Our experiments demonstrate that structured, attribute-rich descriptions significantly enhance retrieval accuracy, particularly for visually complex fabric classes, with the Perception Encoder outperforming other models due to its robust feature alignment capabilities. However, zero-shot retrieval remains challenging in this fine-grained domain, underscoring the need for domain-adapted approaches. Our findings highlight the importance of combining technical textual descriptions with advanced VLMs to optimize cross-modal retrieval in industrial applications.
| null |
https://arxiv.org/abs/2507.04735v1
|
https://arxiv.org/pdf/2507.04735v1.pdf
| null |
[
"Francesco Giuliari",
"Asif Khan Pattan",
"Mohamed Lamine Mekhalfi",
"Fabio Poiesi"
] |
[
"Attribute",
"Cross-Modal Retrieval",
"Image Retrieval",
"Information Retrieval",
"Recommendation Systems",
"Retrieval",
"Zero-shot Text-to-Image Retrieval"
] | 2025-07-07T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/OpenAI/CLIP",
"description": "**Contrastive Language-Image Pre-training** (**CLIP**), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. \r\n\r\nFor pre-training, CLIP is trained to predict which of the $N X N$ possible (image, text) pairings across a batch actually occurred. CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximize the cosine similarity of the image and text embeddings of the $N$ real pairs in the batch while minimizing the cosine similarity of the embeddings of the $N^2 - N$ incorrect pairings. A symmetric cross entropy loss is optimized over these similarity scores. \r\n\r\nImage credit: [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/pdf/2103.00020.pdf)",
"full_name": "Contrastive Language-Image Pre-training",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Representations",
"parent": null
},
"name": "CLIP",
"source_title": "Learning Transferable Visual Models From Natural Language Supervision",
"source_url": "https://arxiv.org/abs/2103.00020v1"
}
] |
https://paperswithcode.com/paper/a-survey-on-proactive-defense-strategies
|
2507.05288
| null | null |
A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models
|
The widespread deployment of large language models (LLMs) across critical domains has amplified the societal risks posed by algorithmically generated misinformation. Unlike traditional false content, LLM-generated misinformation can be self-reinforcing, highly plausible, and capable of rapid propagation across multiple languages, which traditional detection methods fail to mitigate effectively. This paper introduces a proactive defense paradigm, shifting from passive post hoc detection to anticipatory mitigation strategies. We propose a Three Pillars framework: (1) Knowledge Credibility, fortifying the integrity of training and deployed data; (2) Inference Reliability, embedding self-corrective mechanisms during reasoning; and (3) Input Robustness, enhancing the resilience of model interfaces against adversarial attacks. Through a comprehensive survey of existing techniques and a comparative meta-analysis, we demonstrate that proactive defense strategies offer up to 63\% improvement over conventional methods in misinformation prevention, despite non-trivial computational overhead and generalization challenges. We argue that future research should focus on co-designing robust knowledge foundations, reasoning certification, and attack-resistant interfaces to ensure LLMs can effectively counter misinformation across varied domains.
| null |
https://arxiv.org/abs/2507.05288v1
|
https://arxiv.org/pdf/2507.05288v1.pdf
| null |
[
"Shuliang Liu",
"Hongyi Liu",
"Aiwei Liu",
"Bingchen Duan",
"Qi Zheng",
"Yibo Yan",
"He Geng",
"Peijie Jiang",
"Jia Liu",
"Xuming Hu"
] |
[
"Misinformation"
] | 2025-07-05T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/UCSC-REAL/HOC",
"description": "",
"full_name": "High-Order Consensuses",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Value Function Estimation",
"parent": null
},
"name": "HOC",
"source_title": "Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels",
"source_url": "https://arxiv.org/abs/2102.05291v2"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/behaviour-space-analysis-of-llm-driven-meta
|
2507.03605
| null | null |
Behaviour Space Analysis of LLM-driven Meta-heuristic Discovery
|
We investigate the behaviour space of meta-heuristic optimisation algorithms automatically generated by Large Language Model driven algorithm discovery methods. Using the Large Language Evolutionary Algorithm (LLaMEA) framework with a GPT o4-mini LLM, we iteratively evolve black-box optimisation heuristics, evaluated on 10 functions from the BBOB benchmark suite. Six LLaMEA variants, featuring different mutation prompt strategies, are compared and analysed. We log dynamic behavioural metrics including exploration, exploitation, convergence and stagnation measures, for each run, and analyse these via visual projections and network-based representations. Our analysis combines behaviour-based projections, Code Evolution Graphs built from static code features, performance convergence curves, and behaviour-based Search Trajectory Networks. The results reveal clear differences in search dynamics and algorithm structures across LLaMEA configurations. Notably, the variant that employs both a code simplification prompt and a random perturbation prompt in a 1+1 elitist evolution strategy, achieved the best performance, with the highest Area Over the Convergence Curve. Behaviour-space visualisations show that higher-performing algorithms exhibit more intensive exploitation behaviour and faster convergence with less stagnation. Our findings demonstrate how behaviour-space analysis can explain why certain LLM-designed heuristics outperform others and how LLM-driven algorithm discovery navigates the open-ended and complex search space of algorithms. These findings provide insights to guide the future design of adaptive LLM-driven algorithm generators.
| null |
https://arxiv.org/abs/2507.03605v1
|
https://arxiv.org/pdf/2507.03605v1.pdf
| null |
[
"Niki van Stein",
"Haoran Yin",
"Anna V. Kononova",
"Thomas Bäck",
"Gabriela Ochoa"
] |
[
"Large Language Model"
] | 2025-07-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "",
"description": "“How do I get a full refund from Expedia?\r\nHow do I get a full refund from Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!\r\n\r\n\r\n“How do I get a full refund from Expedia?\r\nHow do I get a full refund from Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!",
"full_name": "Refunds@Expedia|||How do I get a full refund from Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Refunds@Expedia|||How do I get a full refund from Expedia?",
"source_title": "Gaussian Error Linear Units (GELUs)",
"source_url": "https://arxiv.org/abs/1606.08415v5"
},
{
"code_snippet_url": "https://github.com/huggingface/transformers/blob/4dc65591b5c61d75c3ef3a2a883bf1433e08fc45/src/transformers/modeling_tf_bert.py#L271",
"description": "**Attention Dropout** is a type of [dropout](https://paperswithcode.com/method/dropout) used in attention-based architectures, where elements are randomly dropped out of the [softmax](https://paperswithcode.com/method/softmax) in the attention equation. For example, for scaled-dot product attention, we would drop elements from the first term:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$",
"full_name": "Attention Dropout",
"introduced_year": 2018,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Attention Dropout",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Cosine Annealing** is a type of learning rate schedule that has the effect of starting with a large learning rate that is relatively rapidly decreased to a minimum value before being increased rapidly again. The resetting of the learning rate acts like a simulated restart of the learning process and the re-use of good weights as the starting point of the restart is referred to as a \"warm restart\" in contrast to a \"cold restart\" where a new set of small random numbers may be used as a starting point.\r\n\r\n$$\\eta\\_{t} = \\eta\\_{min}^{i} + \\frac{1}{2}\\left(\\eta\\_{max}^{i}-\\eta\\_{min}^{i}\\right)\\left(1+\\cos\\left(\\frac{T\\_{cur}}{T\\_{i}}\\pi\\right)\\right)\r\n$$\r\n\r\nWhere where $\\eta\\_{min}^{i}$ and $ \\eta\\_{max}^{i}$ are ranges for the learning rate, and $T\\_{cur}$ account for how many epochs have been performed since the last restart.\r\n\r\nText Source: [Jason Brownlee](https://machinelearningmastery.com/snapshot-ensemble-deep-learning-neural-network/)\r\n\r\nImage Source: [Gao Huang](https://www.researchgate.net/figure/Training-loss-of-100-layer-DenseNet-on-CIFAR10-using-standard-learning-rate-blue-and-M_fig2_315765130)",
"full_name": "Cosine Annealing",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Learning Rate Schedules** refer to schedules for the learning rate during the training of neural networks. Below you can find a continuously updating list of learning rate schedules.",
"name": "Learning Rate Schedules",
"parent": null
},
"name": "Cosine Annealing",
"source_title": "SGDR: Stochastic Gradient Descent with Warm Restarts",
"source_url": "http://arxiv.org/abs/1608.03983v5"
},
{
"code_snippet_url": null,
"description": "**Linear Warmup With Cosine Annealing** is a learning rate schedule where we increase the learning rate linearly for $n$ updates and then anneal according to a cosine schedule afterwards.",
"full_name": "Linear Warmup With Cosine Annealing",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Learning Rate Schedules** refer to schedules for the learning rate during the training of neural networks. Below you can find a continuously updating list of learning rate schedules.",
"name": "Learning Rate Schedules",
"parent": null
},
"name": "Linear Warmup With Cosine Annealing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/fastai/fastai/blob/43001e17ba469308e9688dfe99a891018bcf7ad4/courses/dl2/imdb_scripts/finetune_lm.py#L132",
"description": "**Discriminative Fine-Tuning** is a fine-tuning strategy that is used for [ULMFiT](https://paperswithcode.com/method/ulmfit) type models. Instead of using the same learning rate for all layers of the model, discriminative fine-tuning allows us to tune each layer with different learning rates. For context, the regular stochastic gradient descent ([SGD](https://paperswithcode.com/method/sgd)) update of a model’s parameters $\\theta$ at time step $t$ looks like the following (Ruder, 2016):\r\n\r\n$$ \\theta\\_{t} = \\theta\\_{t-1} − \\eta\\cdot\\nabla\\_{\\theta}J\\left(\\theta\\right)$$\r\n\r\nwhere $\\eta$ is the learning rate and $\\nabla\\_{\\theta}J\\left(\\theta\\right)$ is the gradient with regard to the model’s objective function. For discriminative fine-tuning, we split the parameters $\\theta$ into {$\\theta\\_{1}, \\ldots, \\theta\\_{L}$} where $\\theta\\_{l}$ contains the parameters of the model at the $l$-th layer and $L$ is the number of layers of the model. Similarly, we obtain {$\\eta\\_{1}, \\ldots, \\eta\\_{L}$} where $\\theta\\_{l}$ where $\\eta\\_{l}$ is the learning rate of the $l$-th layer. The SGD update with discriminative finetuning is then:\r\n\r\n$$ \\theta\\_{t}^{l} = \\theta\\_{t-1}^{l} - \\eta^{l}\\cdot\\nabla\\_{\\theta^{l}}J\\left(\\theta\\right) $$\r\n\r\nThe authors find that empirically it worked well to first choose the learning rate $\\eta^{L}$ of the last layer by fine-tuning only the last layer and using $\\eta^{l-1}=\\eta^{l}/2.6$ as the learning rate for lower layers.",
"full_name": "Discriminative Fine-Tuning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Fine-Tuning** methods in deep learning take existing trained networks and 'fine-tune' them to a new task so that information contained in the weights can be repurposed. Below you can find a continuously updating list of fine-tuning methods.",
"name": "Fine-Tuning",
"parent": null
},
"name": "Discriminative Fine-Tuning",
"source_title": "Universal Language Model Fine-tuning for Text Classification",
"source_url": "http://arxiv.org/abs/1801.06146v5"
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": null,
"description": "**GPT** is a [Transformer](https://paperswithcode.com/method/transformer)-based architecture and training procedure for natural language processing tasks. Training follows a two-stage procedure. First, a language modeling objective is used on\r\nthe unlabeled data to learn the initial parameters of a neural network model. Subsequently, these parameters are adapted to a target task using the corresponding supervised objective.",
"full_name": "GPT",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "GPT",
"source_title": "Improving Language Understanding by Generative Pre-Training",
"source_url": "https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf"
}
] |
https://paperswithcode.com/paper/meta-secalign-a-secure-foundation-llm-against
|
2507.02735
| null | null |
Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks
|
Prompt injection attacks pose a significant security threat to LLM-integrated applications. Model-level defenses have shown strong effectiveness, but are currently deployed into commercial-grade models in a closed-source manner. We believe open-source models are needed by the AI security community, where co-development of attacks and defenses through open research drives scientific progress in mitigation against prompt injection attacks. To this end, we develop Meta SecAlign, the first open-source and open-weight LLM with built-in model-level defense that achieves commercial-grade model performance. We provide complete details of our training recipe, which utilizes an improved version of the SOTA SecAlign defense. Evaluations on 9 utility benchmarks and 7 security benchmarks show that Meta SecAlign, despite being trained on a generic instruction-tuning dataset, confers security in unseen downstream tasks, including tool-calling and agentic web navigation, in addition general instruction-following. Our best model -- Meta-SecAlign-70B -- achieves state-of-the-art robustness against prompt injection attacks and comparable utility to closed-source commercial LLM with model-level defense.
|
We believe open-source models are needed by the AI security community, where co-development of attacks and defenses through open research drives scientific progress in mitigation against prompt injection attacks.
|
https://arxiv.org/abs/2507.02735v1
|
https://arxiv.org/pdf/2507.02735v1.pdf
| null |
[
"Sizhe Chen",
"Arman Zharmagambetov",
"David Wagner",
"Chuan Guo"
] |
[
"Instruction Following"
] | 2025-07-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/high-order-deep-meta-learning-with-category
|
2507.02634
| null | null |
High-Order Deep Meta-Learning with Category-Theoretic Interpretation
|
We introduce a new hierarchical deep learning framework for recursive higher-order meta-learning that enables neural networks (NNs) to construct, solve, and generalise across hierarchies of tasks. Central to this approach is a generative mechanism that creates \emph{virtual tasks} -- synthetic problem instances designed to enable the meta-learner to learn \emph{soft constraints} and unknown generalisable rules across related tasks. Crucially, this enables the framework to generate its own informative, task-grounded datasets thereby freeing machine learning (ML) training from the limitations of relying entirely on human-generated data. By actively exploring the virtual point landscape and seeking out tasks lower-level learners find difficult, the meta-learner iteratively refines constraint regions. This enhances inductive biases, regularises the adaptation process, and produces novel, unanticipated tasks and constraints required for generalisation. Each meta-level of the hierarchy corresponds to a progressively abstracted generalisation of problems solved at lower levels, enabling a structured and interpretable learning progression. By interpreting meta-learners as category-theoretic \emph{functors} that generate and condition a hierarchy of subordinate learners, we establish a compositional structure that supports abstraction and knowledge transfer across progressively generalised tasks. The category-theoretic perspective unifies existing meta-learning models and reveals how learning processes can be transformed and compared through functorial relationships, while offering practical design principles for structuring meta-learning. We speculate this architecture may underpin the next generation of NNs capable of autonomously generating novel, instructive tasks and their solutions, thereby advancing ML towards general artificial intelligence.
| null |
https://arxiv.org/abs/2507.02634v1
|
https://arxiv.org/pdf/2507.02634v1.pdf
| null |
[
"David H. Mguni"
] |
[
"Meta-Learning",
"Transfer Learning"
] | 2025-07-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/automated-grading-of-students-handwritten
|
2507.03056
| null | null |
Automated Grading of Students' Handwritten Graphs: A Comparison of Meta-Learning and Vision-Large Language Models
|
With the rise of online learning, the demand for efficient and consistent assessment in mathematics has significantly increased over the past decade. Machine Learning (ML), particularly Natural Language Processing (NLP), has been widely used for autograding student responses, particularly those involving text and/or mathematical expressions. However, there has been limited research on autograding responses involving students' handwritten graphs, despite their prevalence in Science, Technology, Engineering, and Mathematics (STEM) curricula. In this study, we implement multimodal meta-learning models for autograding images containing students' handwritten graphs and text. We further compare the performance of Vision Large Language Models (VLLMs) with these specially trained metalearning models. Our results, evaluated on a real-world dataset collected from our institution, show that the best-performing meta-learning models outperform VLLMs in 2-way classification tasks. In contrast, in more complex 3-way classification tasks, the best-performing VLLMs slightly outperform the meta-learning models. While VLLMs show promising results, their reliability and practical applicability remain uncertain and require further investigation.
| null |
https://arxiv.org/abs/2507.03056v1
|
https://arxiv.org/pdf/2507.03056v1.pdf
| null |
[
"Behnam Parsaeifard",
"Martin Hlosta",
"Per Bergamin"
] |
[
"Meta-Learning"
] | 2025-07-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/mc-inr-efficient-encoding-of-multivariate
|
2507.02494
| null | null |
MC-INR: Efficient Encoding of Multivariate Scientific Simulation Data using Meta-Learning and Clustered Implicit Neural Representations
|
Implicit Neural Representations (INRs) are widely used to encode data as continuous functions, enabling the visualization of large-scale multivariate scientific simulation data with reduced memory usage. However, existing INR-based methods face three main limitations: (1) inflexible representation of complex structures, (2) primarily focusing on single-variable data, and (3) dependence on structured grids. Thus, their performance degrades when applied to complex real-world datasets. To address these limitations, we propose a novel neural network-based framework, MC-INR, which handles multivariate data on unstructured grids. It combines meta-learning and clustering to enable flexible encoding of complex structures. To further improve performance, we introduce a residual-based dynamic re-clustering mechanism that adaptively partitions clusters based on local error. We also propose a branched layer to leverage multivariate data through independent branches simultaneously. Experimental results demonstrate that MC-INR outperforms existing methods on scientific data encoding tasks.
| null |
https://arxiv.org/abs/2507.02494v1
|
https://arxiv.org/pdf/2507.02494v1.pdf
| null |
[
"Hyunsoo Son",
"Jeonghyun Noh",
"Suemin Jeon",
"Chaoli Wang",
"Won-Ki Jeong"
] |
[
"Clustering",
"Meta-Learning"
] | 2025-07-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/determination-of-structural-cracks-using-deep
|
2507.02416
| null | null |
Determination Of Structural Cracks Using Deep Learning Frameworks
|
Structural crack detection is a critical task for public safety as it helps in preventing potential structural failures that could endanger lives. Manual detection by inexperienced personnel can be slow, inconsistent, and prone to human error, which may compromise the reliability of assessments. The current study addresses these challenges by introducing a novel deep-learning architecture designed to enhance the accuracy and efficiency of structural crack detection. In this research, various configurations of residual U-Net models were utilized. These models, due to their robustness in capturing fine details, were further integrated into an ensemble with a meta-model comprising convolutional blocks. This unique combination aimed to boost prediction efficiency beyond what individual models could achieve. The ensemble's performance was evaluated against well-established architectures such as SegNet and the traditional U-Net. Results demonstrated that the residual U-Net models outperformed their predecessors, particularly with low-resolution imagery, and the ensemble model exceeded the performance of individual models, proving it as the most effective. The assessment was based on the Intersection over Union (IoU) metric and DICE coefficient. The ensemble model achieved the highest scores, signifying superior accuracy. This advancement suggests way for more reliable automated systems in structural defects monitoring tasks.
| null |
https://arxiv.org/abs/2507.02416v1
|
https://arxiv.org/pdf/2507.02416v1.pdf
| null |
[
"Subhasis Dasgupta",
"Jaydip Sen",
"Tuhina Halder"
] |
[
"Deep Learning"
] | 2025-07-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/yassouali/pytorch_segmentation/blob/8b8e3ee20a3aa733cb19fc158ad5d7773ed6da7f/models/segnet.py#L9",
"description": "**SegNet** is a semantic segmentation model. This core trainable segmentation architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the\r\nVGG16 network. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature maps. Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to\r\nperform non-linear upsampling.",
"full_name": "SegNet",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Semantic Segmentation Models** are a class of methods that address the task of semantically segmenting an image into different object classes. Below you can find a continuously updating list of semantic segmentation models. ",
"name": "Semantic Segmentation Models",
"parent": null
},
"name": "SegNet",
"source_title": "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation",
"source_url": "http://arxiv.org/abs/1511.00561v3"
}
] |
https://paperswithcode.com/paper/evaluating-the-promise-and-pitfalls-of-llms
|
2507.02087
| null | null |
Evaluating the Promise and Pitfalls of LLMs in Hiring Decisions
|
The use of large language models (LLMs) in hiring promises to streamline candidate screening, but it also raises serious concerns regarding accuracy and algorithmic bias where sufficient safeguards are not in place. In this work, we benchmark several state-of-the-art foundational LLMs - including models from OpenAI, Anthropic, Google, Meta, and Deepseek, and compare them with our proprietary domain-specific hiring model (Match Score) for job candidate matching. We evaluate each model's predictive accuracy (ROC AUC, Precision-Recall AUC, F1-score) and fairness (impact ratio of cut-off analysis across declared gender, race, and intersectional subgroups). Our experiments on a dataset of roughly 10,000 real-world recent candidate-job pairs show that Match Score outperforms the general-purpose LLMs on accuracy (ROC AUC 0.85 vs 0.77) and achieves significantly more equitable outcomes across demographic groups. Notably, Match Score attains a minimum race-wise impact ratio of 0.957 (near-parity), versus 0.809 or lower for the best LLMs, (0.906 vs 0.773 for the intersectionals, respectively). We discuss why pretraining biases may cause LLMs with insufficient safeguards to propagate societal biases in hiring scenarios, whereas a bespoke supervised model can more effectively mitigate these biases. Our findings highlight the importance of domain-specific modeling and bias auditing when deploying AI in high-stakes domains such as hiring, and caution against relying on off-the-shelf LLMs for such tasks without extensive fairness safeguards. Furthermore, we show with empirical evidence that there shouldn't be a dichotomy between choosing accuracy and fairness in hiring: a well-designed algorithm can achieve both accuracy in hiring and fairness in outcomes.
| null |
https://arxiv.org/abs/2507.02087v1
|
https://arxiv.org/pdf/2507.02087v1.pdf
| null |
[
"Eitan Anzenberg",
"Arunava Samajpati",
"Sivasankaran Chandrasekar",
"Varun Kacholia"
] |
[
"Fairness"
] | 2025-07-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/lora-fine-tuning-without-gpus-a-cpu-efficient
|
2507.01806
| null | null |
LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs
|
Low-Rank Adapters (LoRAs) have transformed the fine-tuning of Large Language Models (LLMs) by enabling parameter-efficient updates. However, their widespread adoption remains limited by the reliance on GPU-based training. In this work, we propose a theoretically grounded approach to LoRA fine-tuning designed specifically for users with limited computational resources, particularly those restricted to standard laptop CPUs. Our method learns a meta-operator that maps any input dataset, represented as a probability distribution, to a set of LoRA weights by leveraging a large bank of pre-trained adapters for the Mistral-7B-Instruct-v0.2 model. Instead of performing new gradient-based updates, our pipeline constructs adapters via lightweight combinations of existing LoRAs directly on CPU. While the resulting adapters do not match the performance of GPU-trained counterparts, they consistently outperform the base Mistral model on downstream tasks, offering a practical and accessible alternative to traditional GPU-based fine-tuning.
| null |
https://arxiv.org/abs/2507.01806v1
|
https://arxiv.org/pdf/2507.01806v1.pdf
| null |
[
"Reza Arabpour",
"Haitz Sáez de Ocáriz Borde",
"Anastasis Kratsios"
] |
[
"CPU",
"GPU"
] | 2025-07-02T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
}
] |
https://paperswithcode.com/paper/meta-emulation-an-application-to-the-social
|
2507.01804
| null | null |
Meta-emulation: An application to the social cost of carbon
|
A large database of published model results is used to estimate the distribution of the social cost of carbon as a function of the underlying assumptions. The literature on the social cost of carbon deviates in its assumptions from the literatures on the impacts of climate change, discounting, and risk aversion. The proposed meta-emulator corrects this. The social cost of carbon is higher than reported in the literature.
| null |
https://arxiv.org/abs/2507.01804v1
|
https://arxiv.org/pdf/2507.01804v1.pdf
| null |
[
"Richard S. J. Tol"
] |
[] | 2025-07-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/cast-phys-contactless-affective-states
|
2507.06080
| null | null |
CAST-Phys: Contactless Affective States Through Physiological signals Database
|
In recent years, affective computing and its applications have become a fast-growing research topic. Despite significant advancements, the lack of affective multi-modal datasets remains a major bottleneck in developing accurate emotion recognition systems. Furthermore, the use of contact-based devices during emotion elicitation often unintentionally influences the emotional experience, reducing or altering the genuine spontaneous emotional response. This limitation highlights the need for methods capable of extracting affective cues from multiple modalities without physical contact, such as remote physiological emotion recognition. To address this, we present the Contactless Affective States Through Physiological Signals Database (CAST-Phys), a novel high-quality dataset explicitly designed for multi-modal remote physiological emotion recognition using facial and physiological cues. The dataset includes diverse physiological signals, such as photoplethysmography (PPG), electrodermal activity (EDA), and respiration rate (RR), alongside high-resolution uncompressed facial video recordings, enabling the potential for remote signal recovery. Our analysis highlights the crucial role of physiological signals in realistic scenarios where facial expressions alone may not provide sufficient emotional information. Furthermore, we demonstrate the potential of remote multi-modal emotion recognition by evaluating the impact of individual and fused modalities, showcasing its effectiveness in advancing contactless emotion recognition technologies.
| null |
https://arxiv.org/abs/2507.06080v1
|
https://arxiv.org/pdf/2507.06080v1.pdf
| null |
[
"Joaquim Comas",
"Alexander Joel Vera",
"Xavier Vives",
"Eleonora De Filippi",
"Alexandre Pereda",
"Federico Sukno"
] |
[
"Emotion Recognition",
"Photoplethysmography (PPG)"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/mcam-multimodal-causal-analysis-model-for-ego
|
2507.06072
| null | null |
MCAM: Multimodal Causal Analysis Model for Ego-Vehicle-Level Driving Video Understanding
|
Accurate driving behavior recognition and reasoning are critical for autonomous driving video understanding. However, existing methods often tend to dig out the shallow causal, fail to address spurious correlations across modalities, and ignore the ego-vehicle level causality modeling. To overcome these limitations, we propose a novel Multimodal Causal Analysis Model (MCAM) that constructs latent causal structures between visual and language modalities. Firstly, we design a multi-level feature extractor to capture long-range dependencies. Secondly, we design a causal analysis module that dynamically models driving scenarios using a directed acyclic graph (DAG) of driving states. Thirdly, we utilize a vision-language transformer to align critical visual features with their corresponding linguistic expressions. Extensive experiments on the BDD-X, and CoVLA datasets demonstrate that MCAM achieves SOTA performance in visual-language causal relationship learning. Furthermore, the model exhibits superior capability in capturing causal characteristics within video sequences, showcasing its effectiveness for autonomous driving applications. The code is available at https://github.com/SixCorePeach/MCAM.
|
Accurate driving behavior recognition and reasoning are critical for autonomous driving video understanding.
|
https://arxiv.org/abs/2507.06072v1
|
https://arxiv.org/pdf/2507.06072v1.pdf
| null |
[
"Tongtong Cheng",
"Rongzhen Li",
"Yixin Xiong",
"Tao Zhang",
"Jing Wang",
"Kai Liu"
] |
[
"Autonomous Driving",
"Video Understanding"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In the ALIGN method, visual and language representations are jointly trained from noisy image alt-text data. The image and text encoders are learned via contrastive loss (formulated as normalized softmax) that pushes the embeddings of the matched image-text pair together and pushing those of non-matched image-text pair apart. The model learns to align visual and language representations of the image and text pairs using the contrastive loss. The representations can be used for vision-only or vision-language task transfer. Without any fine-tuning, ALIGN powers zero-shot visual classification and cross-modal search including image-to-text search, text-to image search and even search with joint image+text queries.",
"full_name": "ALIGN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "Involves models that adapt pre-training to the field of Vision-and-Language (V-L) learning and improve the performance on downstream tasks like visual question answering and visual captioning.\r\n\r\nAccording to [Du et al. (2022)](https://arxiv.org/pdf/2202.10936.pdf), information coming from the different modalities can be encoded in three ways: fusion encoder, dual encoder, and a combination of both. \r\n\r\nReferences:\r\n\r\n- [A Survey of Vision-Language Pre-Trained Models](https://arxiv.org/pdf/2202.10936.pdf)\r\n- [Vision Language models: towards multi-modal deep learning](https://theaisummer.com/vision-language-models/)",
"name": "Vision and Language Pre-Trained Models",
"parent": null
},
"name": "ALIGN",
"source_title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision",
"source_url": "https://arxiv.org/abs/2102.05918v2"
}
] |
https://paperswithcode.com/paper/robust-speech-workload-estimation-for
|
2507.05985
| null | null |
Robust Speech-Workload Estimation for Intelligent Human-Robot Systems
|
Demanding task environments (e.g., supervising a remotely piloted aircraft) require performing tasks quickly and accurately; however, periods of low and high operator workload can decrease task performance. Intelligent modulation of the system's demands and interaction modality in response to changes in operator workload state may increase performance by avoiding undesirable workload states. This system requires real-time estimation of each workload component (i.e., cognitive, physical, visual, speech, and auditory) to adapt the correct modality. Existing workload systems estimate multiple workload components post-hoc, but few estimate speech workload, or function in real-time. An algorithm to estimate speech workload and mitigate undesirable workload states in real-time is presented. An analysis of the algorithm's accuracy is presented, along with the results demonstrating the algorithm's generalizability across individuals and human-machine teaming paradigms. Real-time speech workload estimation is a crucial element towards developing adaptive human-machine systems.
| null |
https://arxiv.org/abs/2507.05985v1
|
https://arxiv.org/pdf/2507.05985v1.pdf
| null |
[
"Julian Fortune",
"Julie A. Adams",
"Jamison Heard"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/high-resolution-visual-reasoning-via-multi
|
2507.05920
| null | null |
High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning
|
State-of-the-art large multi-modal models (LMMs) face challenges when processing high-resolution images, as these inputs are converted into enormous visual tokens, many of which are irrelevant to the downstream task. In this paper, we propose Multi-turn Grounding-based Policy Optimization (MGPO), an end-to-end reinforcement learning (RL) framework that enables LMMs to iteratively focus on key visual regions by automatically cropping sub-images, based on model-predicted grounding coordinates within a multi-turn conversation framework. Compared to supervised fine-tuning (SFT), which requires costly additional grounding annotations, our approach highlights that LMMs can emerge robust grounding abilities during the RL training process, leveraging only a binary reward function derived from the correctness of the final answer. Additionally, we observe that LMMs struggle to autonomously trigger visual grounding during the rollout process. To address this cold start problem, we design a multi-turn conversational template and restrict policy loss computation to model outputs generated across multiple dialogue rounds, thereby promoting stable optimization. Extensive experiments demonstrate that, when trained on standard visual-question-short answering data without grounding annotations, MGPO effectively elicits stronger grounding capabilities compared to GRPO, leading to 5.4\% improvement on in-distribution MME-Realworld and 5.2\% improvement on the challenging out-of-distribution (OOD) V* Bench. Notably, MGPO post-training on Qwen2.5-VL-7B with 21K samples surpasses OpenAI's o1 and GPT-4o models on the OOD V* Bench. Codes are available at https://github.com/EvolvingLMMs-Lab/MGPO.
|
State-of-the-art large multi-modal models (LMMs) face challenges when processing high-resolution images, as these inputs are converted into enormous visual tokens, many of which are irrelevant to the downstream task.
|
https://arxiv.org/abs/2507.05920v1
|
https://arxiv.org/pdf/2507.05920v1.pdf
| null |
[
"Xinyu Huang",
"Yuhao Dong",
"Weiwei Tian",
"Bo Li",
"Rui Feng",
"Ziwei Liu"
] |
[
"MME",
"Reinforcement Learning (RL)",
"Visual Grounding",
"Visual Reasoning"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/musiscene-leveraging-mu-llama-for-scene
|
2507.05894
| null | null |
MusiScene: Leveraging MU-LLaMA for Scene Imagination and Enhanced Video Background Music Generation
|
Humans can imagine various atmospheres and settings when listening to music, envisioning movie scenes that complement each piece. For example, slow, melancholic music might evoke scenes of heartbreak, while upbeat melodies suggest celebration. This paper explores whether a Music Language Model, e.g. MU-LLaMA, can perform a similar task, called Music Scene Imagination (MSI), which requires cross-modal information from video and music to train. To improve upon existing music captioning models which focusing solely on musical elements, we introduce MusiScene, a music captioning model designed to imagine scenes that complement each music. In this paper, (1) we construct a large-scale video-audio caption dataset with 3,371 pairs, (2) we finetune Music Understanding LLaMA for the MSI task to create MusiScene, and (3) we conduct comprehensive evaluations and prove that our MusiScene is more capable of generating contextually relevant captions compared to MU-LLaMA. We leverage the generated MSI captions to enhance Video Background Music Generation (VBMG) from text.
| null |
https://arxiv.org/abs/2507.05894v1
|
https://arxiv.org/pdf/2507.05894v1.pdf
| null |
[
"Fathinah Izzati",
"Xinyue Li",
"Yuxuan Wu",
"Gus Xia"
] |
[
"Language Modeling",
"Language Modelling",
"Music Captioning",
"Music Generation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**LLaMA** is a collection of foundation language models ranging from 7B to 65B parameters. It is based on the transformer architecture with various improvements that were subsequently proposed. The main difference with the original architecture are listed below.\r\n\r\n- RMSNorm normalizing function is used to improve the training stability, by normalizing the input of each transformer sub-layer, instead of normalizing the output.\r\n- The ReLU non-linearity is replaced by the SwiGLU activation function to improve performance.\r\n- Absolute positional embeddings are removed and instead rotary positional embeddings (RoPE) are added at each layer of the network.",
"full_name": "LLaMA",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Language Models** are models for predicting the next word or character in a document. Below you can find a continuously updating list of language models.\r\n\r\n",
"name": "Language Models",
"parent": null
},
"name": "LLaMA",
"source_title": "LLaMA: Open and Efficient Foundation Language Models",
"source_url": "https://arxiv.org/abs/2302.13971v1"
}
] |
https://paperswithcode.com/paper/talkfashion-intelligent-virtual-try-on
|
2507.05790
| null | null |
TalkFashion: Intelligent Virtual Try-On Assistant Based on Multimodal Large Language Model
|
Virtual try-on has made significant progress in recent years. This paper addresses how to achieve multifunctional virtual try-on guided solely by text instructions, including full outfit change and local editing. Previous methods primarily relied on end-to-end networks to perform single try-on tasks, lacking versatility and flexibility. We propose TalkFashion, an intelligent try-on assistant that leverages the powerful comprehension capabilities of large language models to analyze user instructions and determine which task to execute, thereby activating different processing pipelines accordingly. Additionally, we introduce an instruction-based local repainting model that eliminates the need for users to manually provide masks. With the help of multi-modal models, this approach achieves fully automated local editings, enhancing the flexibility of editing tasks. The experimental results demonstrate better semantic consistency and visual quality compared to the current methods.
| null |
https://arxiv.org/abs/2507.05790v1
|
https://arxiv.org/pdf/2507.05790v1.pdf
| null |
[
"Yujie Hu",
"Xuanyu Zhang",
"Weiqi Li",
"Jian Zhang"
] |
[
"Language Modeling",
"Language Modelling",
"Large Language Model",
"Multimodal Large Language Model",
"Virtual Try-on"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/lead-the-llm-enhanced-planning-system
|
2507.05754
| null | null |
LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving
|
A principal barrier to large-scale deployment of urban autonomous driving systems lies in the prevalence of complex scenarios and edge cases. Existing systems fail to effectively interpret semantic information within traffic contexts and discern intentions of other participants, consequently generating decisions misaligned with skilled drivers' reasoning patterns. We present LeAD, a dual-rate autonomous driving architecture integrating imitation learning-based end-to-end (E2E) frameworks with large language model (LLM) augmentation. The high-frequency E2E subsystem maintains real-time perception-planning-control cycles, while the low-frequency LLM module enhances scenario comprehension through multi-modal perception fusion with HD maps and derives optimal decisions via chain-of-thought (CoT) reasoning when baseline planners encounter capability limitations. Our experimental evaluation in the CARLA Simulator demonstrates LeAD's superior handling of unconventional scenarios, achieving 71 points on Leaderboard V1 benchmark, with a route completion of 93%.
| null |
https://arxiv.org/abs/2507.05754v1
|
https://arxiv.org/pdf/2507.05754v1.pdf
| null |
[
"Yuhang Zhang",
"Jiaqi Liu",
"Chengkai Xu",
"Peng Hang",
"Jian Sun"
] |
[
"Autonomous Driving",
"Imitation Learning",
"Language Modeling",
"Language Modelling",
"Large Language Model"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Proximal Policy Optimization**, or **PPO**, is a policy gradient method for reinforcement learning. The motivation was to have an algorithm with the data efficiency and reliable performance of [TRPO](https://paperswithcode.com/method/trpo), while using only first-order optimization. \r\n\r\nLet $r\\_{t}\\left(\\theta\\right)$ denote the probability ratio $r\\_{t}\\left(\\theta\\right) = \\frac{\\pi\\_{\\theta}\\left(a\\_{t}\\mid{s\\_{t}}\\right)}{\\pi\\_{\\theta\\_{old}}\\left(a\\_{t}\\mid{s\\_{t}}\\right)}$, so $r\\left(\\theta\\_{old}\\right) = 1$. TRPO maximizes a “surrogate” objective:\r\n\r\n$$ L^{\\text{CPI}}\\left({\\theta}\\right) = \\hat{\\mathbb{E}}\\_{t}\\left[\\frac{\\pi\\_{\\theta}\\left(a\\_{t}\\mid{s\\_{t}}\\right)}{\\pi\\_{\\theta\\_{old}}\\left(a\\_{t}\\mid{s\\_{t}}\\right)})\\hat{A}\\_{t}\\right] = \\hat{\\mathbb{E}}\\_{t}\\left[r\\_{t}\\left(\\theta\\right)\\hat{A}\\_{t}\\right] $$\r\n\r\nWhere $CPI$ refers to a conservative policy iteration. Without a constraint, maximization of $L^{CPI}$ would lead to an excessively large policy update; hence, we PPO modifies the objective, to penalize changes to the policy that move $r\\_{t}\\left(\\theta\\right)$ away from 1:\r\n\r\n$$ J^{\\text{CLIP}}\\left({\\theta}\\right) = \\hat{\\mathbb{E}}\\_{t}\\left[\\min\\left(r\\_{t}\\left(\\theta\\right)\\hat{A}\\_{t}, \\text{clip}\\left(r\\_{t}\\left(\\theta\\right), 1-\\epsilon, 1+\\epsilon\\right)\\hat{A}\\_{t}\\right)\\right] $$\r\n\r\nwhere $\\epsilon$ is a hyperparameter, say, $\\epsilon = 0.2$. The motivation for this objective is as follows. The first term inside the min is $L^{CPI}$. The second term, $\\text{clip}\\left(r\\_{t}\\left(\\theta\\right), 1-\\epsilon, 1+\\epsilon\\right)\\hat{A}\\_{t}$ modifies the surrogate\r\nobjective by clipping the probability ratio, which removes the incentive for moving $r\\_{t}$ outside of the interval $\\left[1 − \\epsilon, 1 + \\epsilon\\right]$. Finally, we take the minimum of the clipped and unclipped objective, so the final objective is a lower bound (i.e., a pessimistic bound) on the unclipped objective. With this scheme, we only ignore the change in probability ratio when it would make the objective improve, and we include it when it makes the objective worse. \r\n\r\nOne detail to note is that when we apply PPO for a network where we have shared parameters for actor and critic functions, we typically add to the objective function an error term on value estimation and an entropy term to encourage exploration.",
"full_name": "Proximal Policy Optimization",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "**Policy Gradient Methods** try to optimize the policy function directly in reinforcement learning. This contrasts with, for example, Q-Learning, where the policy manifests itself as maximizing a value function. Below you can find a continuously updating catalog of policy gradient methods.",
"name": "Policy Gradient Methods",
"parent": null
},
"name": "PPO",
"source_title": "Proximal Policy Optimization Algorithms",
"source_url": "http://arxiv.org/abs/1707.06347v2"
},
{
"code_snippet_url": "",
"description": "CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. \r\n\r\nSource: [Dosovitskiy et al.](https://arxiv.org/pdf/1711.03938v1.pdf)\r\n\r\nImage source: [Dosovitskiy et al.](https://arxiv.org/pdf/1711.03938v1.pdf)",
"full_name": "CARLA: An Open Urban Driving Simulator",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Video Game Models",
"parent": null
},
"name": "CARLA",
"source_title": "CARLA: An Open Urban Driving Simulator",
"source_url": "http://arxiv.org/abs/1711.03938v1"
}
] |
https://paperswithcode.com/paper/event-rgb-fusion-for-spacecraft-pose
|
2507.05698
| null | null |
Event-RGB Fusion for Spacecraft Pose Estimation Under Harsh Lighting
|
Spacecraft pose estimation is crucial for autonomous in-space operations, such as rendezvous, docking and on-orbit servicing. Vision-based pose estimation methods, which typically employ RGB imaging sensors, is a compelling solution for spacecraft pose estimation, but are challenged by harsh lighting conditions, which produce imaging artifacts such as glare, over-exposure, blooming and lens flare. Due to their much higher dynamic range, neuromorphic or event sensors are more resilient to extreme lighting conditions. However, event sensors generally have lower spatial resolution and suffer from reduced signal-to-noise ratio during periods of low relative motion. This work addresses these individual sensor limitations by introducing a sensor fusion approach combining RGB and event sensors. A beam-splitter prism was employed to achieve precise optical and temporal alignment. Then, a RANSAC-based technique was developed to fuse the information from the RGB and event channels to achieve pose estimation that leveraged the strengths of the two modalities. The pipeline was complemented by dropout uncertainty estimation to detect extreme conditions that affect either channel. To benchmark the performance of the proposed event-RGB fusion method, we collected a comprehensive real dataset of RGB and event data for satellite pose estimation in a laboratory setting under a variety of challenging illumination conditions. Encouraging results on the dataset demonstrate the efficacy of our event-RGB fusion approach and further supports the usage of event sensors for spacecraft pose estimation. To support community research on this topic, our dataset will be released publicly.
|
Then, a RANSAC-based technique was developed to fuse the information from the RGB and event channels to achieve pose estimation that leveraged the strengths of the two modalities.
|
https://arxiv.org/abs/2507.05698v1
|
https://arxiv.org/pdf/2507.05698v1.pdf
| null |
[
"Mohsi Jawaid",
"Marcus Märtens",
"Tat-Jun Chin"
] |
[
"Pose Estimation",
"Sensor Fusion",
"Spacecraft Pose Estimation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
}
] |
https://paperswithcode.com/paper/integrated-structural-prompt-learning-for
|
2507.05677
| null | null |
Integrated Structural Prompt Learning for Vision-Language Models
|
Prompt learning methods have significantly extended the transferability of pre-trained Vision-Language Models (VLMs) like CLIP for various downstream tasks. These methods adopt handcraft templates or learnable vectors to provide text or image instructions in fine-tuning VLMs. However, most existing works ignore the structural relationships between learnable prompts and tokens within and between modalities. Moreover, balancing the performance of base and new classes remains a significant challenge. In this paper, we propose an Integrated Structural Prompt (ISP) for VLMs to enhance the interaction of information representations between the text and image branches. ISP introduces self-structural and cross-structural prompt modules to model the structural relationships between learnable prompts and frozen tokens within and across modalities. This enables efficient information transfer while preserving feature stability. Additionally, we propose a sample probing module that dynamically adjusts loss coefficients based on sample difficulty, preventing the mode from overfitting to simple samples and improving generalization ability to new classes. Extensive experiments on three widely used settings: base-to-new generalization, cross-dataset evaluation, and domain generalization demonstrate that the proposed ISP achieves competitive performance against state-of-the-art methods.
| null |
https://arxiv.org/abs/2507.05677v2
|
https://arxiv.org/pdf/2507.05677v2.pdf
| null |
[
"Jiahui Wang",
"Qin Xu",
"Bo Jiang",
"Bin Luo"
] |
[
"Domain Generalization",
"Prompt Learning",
"Sample Probing"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "ADaptive gradient method with the OPTimal convergence rate",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "ADOPT",
"source_title": "ADOPT: Modified Adam Can Converge with Any $β_2$ with the Optimal Rate",
"source_url": "https://arxiv.org/abs/2411.02853v3"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
},
{
"code_snippet_url": "https://github.com/OpenAI/CLIP",
"description": "**Contrastive Language-Image Pre-training** (**CLIP**), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. \r\n\r\nFor pre-training, CLIP is trained to predict which of the $N X N$ possible (image, text) pairings across a batch actually occurred. CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximize the cosine similarity of the image and text embeddings of the $N$ real pairs in the batch while minimizing the cosine similarity of the embeddings of the $N^2 - N$ incorrect pairings. A symmetric cross entropy loss is optimized over these similarity scores. \r\n\r\nImage credit: [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/pdf/2103.00020.pdf)",
"full_name": "Contrastive Language-Image Pre-training",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Representations",
"parent": null
},
"name": "CLIP",
"source_title": "Learning Transferable Visual Models From Natural Language Supervision",
"source_url": "https://arxiv.org/abs/2103.00020v1"
}
] |
https://paperswithcode.com/paper/graph-learning
|
2507.05636
| null | null |
Graph Learning
|
Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining significant momentum with the advent of graph neural networks (GNNs). Over the past decade, progress in scalable architectures, dynamic graph modeling, multimodal learning, generative AI, explainable AI (XAI), and responsible AI has broadened the applicability of graph learning to various challenging environments. Graph learning is significant due to its ability to model complex, non-Euclidean relationships that traditional machine learning struggles to capture, thus better supporting real-world applications ranging from drug discovery and fraud detection to recommender systems and scientific reasoning. However, challenges like scalability, generalization, heterogeneity, interpretability, and trustworthiness must be addressed to unlock its full potential. This survey provides a comprehensive introduction to graph learning, focusing on key dimensions including scalable, temporal, multimodal, generative, explainable, and responsible graph learning. We review state-of-the-art techniques for efficiently handling large-scale graphs, capturing dynamic temporal dependencies, integrating heterogeneous data modalities, generating novel graph samples, and enhancing interpretability to foster trust and transparency. We also explore ethical considerations, such as privacy and fairness, to ensure responsible deployment of graph learning models. Additionally, we identify and discuss emerging topics, highlighting recent integration of graph learning and other AI paradigms and offering insights into future directions. This survey serves as a valuable resource for researchers and practitioners seeking to navigate the rapidly evolving landscape of graph learning.
| null |
https://arxiv.org/abs/2507.05636v1
|
https://arxiv.org/pdf/2507.05636v1.pdf
| null |
[
"Feng Xia",
"Ciyuan Peng",
"Jing Ren",
"Falih Gozi Febrinanto",
"Renqiang Luo",
"Vidya Saikrishna",
"Shuo Yu",
"Xiangjie Kong"
] |
[
"Drug Discovery",
"Fairness",
"Fraud Detection",
"Graph Learning",
"Navigate",
"Recommendation Systems"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
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