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https://paperswithcode.com/paper/admc-attention-based-diffusion-model-for
|
2507.05624
| null | null |
ADMC: Attention-based Diffusion Model for Missing Modalities Feature Completion
|
Multimodal emotion and intent recognition is essential for automated human-computer interaction, It aims to analyze users' speech, text, and visual information to predict their emotions or intent. One of the significant challenges is that missing modalities due to sensor malfunctions or incomplete data. Traditional methods that attempt to reconstruct missing information often suffer from over-coupling and imprecise generation processes, leading to suboptimal outcomes. To address these issues, we introduce an Attention-based Diffusion model for Missing Modalities feature Completion (ADMC). Our framework independently trains feature extraction networks for each modality, preserving their unique characteristics and avoiding over-coupling. The Attention-based Diffusion Network (ADN) generates missing modality features that closely align with authentic multimodal distribution, enhancing performance across all missing-modality scenarios. Moreover, ADN's cross-modal generation offers improved recognition even in full-modality contexts. Our approach achieves state-of-the-art results on the IEMOCAP and MIntRec benchmarks, demonstrating its effectiveness in both missing and complete modality scenarios.
| null |
https://arxiv.org/abs/2507.05624v1
|
https://arxiv.org/pdf/2507.05624v1.pdf
| null |
[
"Wei zhang",
"Juan Chen",
"Yanbo J. Wang",
"En Zhu",
"Xuan Yang",
"Yiduo Wang"
] |
[
"Intent Recognition"
] | 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"
}
] |
https://paperswithcode.com/paper/adaptagen-domain-specific-image-generation
|
2507.05621
| null | null |
AdaptaGen: Domain-Specific Image Generation through Hierarchical Semantic Optimization Framework
|
Domain-specific image generation aims to produce high-quality visual content for specialized fields while ensuring semantic accuracy and detail fidelity. However, existing methods exhibit two critical limitations: First, current approaches address prompt engineering and model adaptation separately, overlooking the inherent dependence between semantic understanding and visual representation in specialized domains. Second, these techniques inadequately incorporate domain-specific semantic constraints during content synthesis, resulting in generation outcomes that exhibit hallucinations and semantic deviations. To tackle these issues, we propose AdaptaGen, a hierarchical semantic optimization framework that integrates matrix-based prompt optimization with multi-perspective understanding, capturing comprehensive semantic relationships from both global and local perspectives. To mitigate hallucinations in specialized domains, we design a cross-modal adaptation mechanism, which, when combined with intelligent content synthesis, enables preserving core thematic elements while incorporating diverse details across images. Additionally, we introduce a two-phase caption semantic transformation during the generation phase. This approach maintains semantic coherence while enhancing visual diversity, ensuring the generated images adhere to domain-specific constraints. Experimental results confirm our approach's effectiveness, with our framework achieving superior performance across 40 categories from diverse datasets using only 16 images per category, demonstrating significant improvements in image quality, diversity, and semantic consistency.
| null |
https://arxiv.org/abs/2507.05621v1
|
https://arxiv.org/pdf/2507.05621v1.pdf
| null |
[
"Suoxiang Zhang",
"Xiaxi Li",
"Hongrui Chang",
"Zhuoyan Hou",
"Guoxin Wu",
"Ronghua Ji"
] |
[
"Diversity",
"Image Generation",
"Prompt Engineering"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/semi-supervised-defect-detection-via
|
2507.05588
| null | null |
Semi-Supervised Defect Detection via Conditional Diffusion and CLIP-Guided Noise Filtering
|
In the realm of industrial quality inspection, defect detection stands as a critical component, particularly in high-precision, safety-critical sectors such as automotive components aerospace, and medical devices. Traditional methods, reliant on manual inspection or early image processing algorithms, suffer from inefficiencies, high costs, and limited robustness. This paper introduces a semi-supervised defect detection framework based on conditional diffusion (DSYM), leveraging a two-stage collaborative training mechanism and a staged joint optimization strategy. The framework utilizes labeled data for initial training and subsequently incorporates unlabeled data through the generation of pseudo-labels. A conditional diffusion model synthesizes multi-scale pseudo-defect samples, while a CLIP cross-modal feature-based noise filtering mechanism mitigates label contamination. Experimental results on the NEU-DET dataset demonstrate a 78.4% [email protected] with the same amount of labeled data as traditional supervised methods, and 75.1% [email protected] with only 40% of the labeled data required by the original supervised model, showcasing significant advantages in data efficiency. This research provides a high-precision, low-labeling-dependent solution for defect detection in industrial quality inspection scenarios. The work of this article has been open-sourced at https://github.com/cLin-c/Semisupervised-DSYM.
|
In the realm of industrial quality inspection, defect detection stands as a critical component, particularly in high-precision, safety-critical sectors such as automotive components aerospace, and medical devices.
|
https://arxiv.org/abs/2507.05588v1
|
https://arxiv.org/pdf/2507.05588v1.pdf
| null |
[
"Shuai Li",
"Shihan Chen",
"Wanru Geng",
"Zhaohua Xu",
"Xiaolu Liu",
"Can Dong",
"Zhen Tian",
"Changlin Chen"
] |
[
"Defect Detection",
"Supervised Defect Detection"
] | 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": "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/multi-modal-face-anti-spoofing-via-cross
|
2507.05575
| null | null |
Multi-Modal Face Anti-Spoofing via Cross-Modal Feature Transitions
|
Multi-modal face anti-spoofing (FAS) aims to detect genuine human presence by extracting discriminative liveness cues from multiple modalities, such as RGB, infrared (IR), and depth images, to enhance the robustness of biometric authentication systems. However, because data from different modalities are typically captured by various camera sensors and under diverse environmental conditions, multi-modal FAS often exhibits significantly greater distribution discrepancies across training and testing domains compared to single-modal FAS. Furthermore, during the inference stage, multi-modal FAS confronts even greater challenges when one or more modalities are unavailable or inaccessible. In this paper, we propose a novel Cross-modal Transition-guided Network (CTNet) to tackle the challenges in the multi-modal FAS task. Our motivation stems from that, within a single modality, the visual differences between live faces are typically much smaller than those of spoof faces. Additionally, feature transitions across modalities are more consistent for the live class compared to those between live and spoof classes. Upon this insight, we first propose learning consistent cross-modal feature transitions among live samples to construct a generalized feature space. Next, we introduce learning the inconsistent cross-modal feature transitions between live and spoof samples to effectively detect out-of-distribution (OOD) attacks during inference. To further address the issue of missing modalities, we propose learning complementary infrared (IR) and depth features from the RGB modality as auxiliary modalities. Extensive experiments demonstrate that the proposed CTNet outperforms previous two-class multi-modal FAS methods across most protocols.
| null |
https://arxiv.org/abs/2507.05575v1
|
https://arxiv.org/pdf/2507.05575v1.pdf
| null |
[
"Jun-Xiong Chong",
"Fang-Yu Hsu",
"Ming-Tsung Hsu",
"Yi-Ting Lin",
"Kai-Heng Chien",
"Chiou-Ting Hsu",
"Pei-Kai Huang"
] |
[
"Face Anti-Spoofing"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/modeling-deontic-modal-operators-with-the-s
|
2507.05519
| null | null |
Modeling (Deontic) Modal Operators With the s(CASP) Goal-directed Predicate Answer Set Programming System
|
We consider the problem of implementing deontic modal logic. We show how (deontic) modal operators can be expressed elegantly using default negation (negation-as-failure) and strong negation present in answer set programming (ASP). We propose using global constraints of ASP to represent obligations and impermissibilities of deontic modal logic. We show that our proposed representation results in the various paradoxes of deontic modal logic being elegantly resolved.
| null |
https://arxiv.org/abs/2507.05519v2
|
https://arxiv.org/pdf/2507.05519v2.pdf
| null |
[
"Gopal Gupta",
"Abhiramon Rajasekharan",
"Alexis R. Tudor",
"Elmer Salazar",
"Joaquín Arias"
] |
[
"Negation"
] | 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/llama-nemoretriever-colembed-top-performing
|
2507.05513
| null | null |
Llama Nemoretriever Colembed: Top-Performing Text-Image Retrieval Model
|
Motivated by the growing demand for retrieval systems that operate across modalities, we introduce llama-nemoretriever-colembed, a unified text-image retrieval model that delivers state-of-the-art performance across multiple benchmarks. We release two model variants, 1B and 3B. The 3B model achieves state of the art performance, scoring NDCG@5 91.0 on ViDoRe V1 and 63.5 on ViDoRe V2, placing first on both leaderboards as of June 27, 2025. Our approach leverages the NVIDIA Eagle2 Vision-Language model (VLM), modifies its architecture by replacing causal attention with bidirectional attention, and integrates a ColBERT-style late interaction mechanism to enable fine-grained multimodal retrieval in a shared embedding space. While this mechanism delivers superior retrieval accuracy, it introduces trade-offs in storage and efficiency. We provide a comprehensive analysis of these trade-offs. Additionally, we adopt a two-stage training strategy to enhance the model's retrieval capabilities.
| null |
https://arxiv.org/abs/2507.05513v1
|
https://arxiv.org/pdf/2507.05513v1.pdf
| null |
[
"Mengyao Xu",
"Gabriel Moreira",
"Ronay Ak",
"Radek Osmulski",
"Yauhen Babakhin",
"Zhiding Yu",
"Benedikt Schifferer",
"Even Oldridge"
] |
[
"Image Retrieval",
"Language Modeling",
"Language Modelling",
"Retrieval"
] | 2025-07-07T00:00:00 | 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"
}
] |
https://paperswithcode.com/paper/neural-driven-image-editing
|
2507.05397
| null | null |
Neural-Driven Image Editing
|
Traditional image editing typically relies on manual prompting, making it labor-intensive and inaccessible to individuals with limited motor control or language abilities. Leveraging recent advances in brain-computer interfaces (BCIs) and generative models, we propose LoongX, a hands-free image editing approach driven by multimodal neurophysiological signals. LoongX utilizes state-of-the-art diffusion models trained on a comprehensive dataset of 23,928 image editing pairs, each paired with synchronized electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), photoplethysmography (PPG), and head motion signals that capture user intent. To effectively address the heterogeneity of these signals, LoongX integrates two key modules. The cross-scale state space (CS3) module encodes informative modality-specific features. The dynamic gated fusion (DGF) module further aggregates these features into a unified latent space, which is then aligned with edit semantics via fine-tuning on a diffusion transformer (DiT). Additionally, we pre-train the encoders using contrastive learning to align cognitive states with semantic intentions from embedded natural language. Extensive experiments demonstrate that LoongX achieves performance comparable to text-driven methods (CLIP-I: 0.6605 vs. 0.6558; DINO: 0.4812 vs. 0.4636) and outperforms them when neural signals are combined with speech (CLIP-T: 0.2588 vs. 0.2549). These results highlight the promise of neural-driven generative models in enabling accessible, intuitive image editing and open new directions for cognitive-driven creative technologies. Datasets and code will be released to support future work and foster progress in this emerging area.
|
Traditional image editing typically relies on manual prompting, making it labor-intensive and inaccessible to individuals with limited motor control or language abilities.
|
https://arxiv.org/abs/2507.05397v1
|
https://arxiv.org/pdf/2507.05397v1.pdf
| null |
[
"Pengfei Zhou",
"Jie Xia",
"Xiaopeng Peng",
"Wangbo Zhao",
"Zilong Ye",
"Zekai Li",
"Suorong Yang",
"Jiadong Pan",
"Yuanxiang Chen",
"Ziqiao Wang",
"Kai Wang",
"Qian Zheng",
"Xiaojun Chang",
"Gang Pan",
"Shurong Dong",
"Kaipeng Zhang",
"Yang You"
] |
[
"Contrastive Learning",
"Multimodel-guided image editing"
] | 2025-07-07T00:00:00 | 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
},
{
"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"
}
] |
https://paperswithcode.com/paper/taming-data-challenges-in-ml-based-security
|
2507.06092
| null | null |
Taming Data Challenges in ML-based Security Tasks: Lessons from Integrating Generative AI
|
Machine learning-based supervised classifiers are widely used for security tasks, and their improvement has been largely focused on algorithmic advancements. We argue that data challenges that negatively impact the performance of these classifiers have received limited attention. We address the following research question: Can developments in Generative AI (GenAI) address these data challenges and improve classifier performance? We propose augmenting training datasets with synthetic data generated using GenAI techniques to improve classifier generalization. We evaluate this approach across 7 diverse security tasks using 6 state-of-the-art GenAI methods and introduce a novel GenAI scheme called Nimai that enables highly controlled data synthesis. We find that GenAI techniques can significantly improve the performance of security classifiers, achieving improvements of up to 32.6% even in severely data-constrained settings (only ~180 training samples). Furthermore, we demonstrate that GenAI can facilitate rapid adaptation to concept drift post-deployment, requiring minimal labeling in the adjustment process. Despite successes, our study finds that some GenAI schemes struggle to initialize (train and produce data) on certain security tasks. We also identify characteristics of specific tasks, such as noisy labels, overlapping class distributions, and sparse feature vectors, which hinder performance boost using GenAI. We believe that our study will drive the development of future GenAI tools designed for security tasks.
| null |
https://arxiv.org/abs/2507.06092v1
|
https://arxiv.org/pdf/2507.06092v1.pdf
| null |
[
"Shravya Kanchi",
"Neal Mangaokar",
"Aravind Cheruvu",
"Sifat Muhammad Abdullah",
"Shirin Nilizadeh",
"Atul Prakash",
"Bimal Viswanath"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/core-enhancing-metacognition-with-label-free
|
2507.06087
| null | null |
CoRE: Enhancing Metacognition with Label-free Self-evaluation in LRMs
|
Large reasoning models (LRMs) have demonstrated impressive capabilities in domains like mathematics and program synthesis. Despite their strong performance, LRMs often exhibit overthinking -- excessive and redundant reasoning steps that introduce inefficiencies during inference. This phenomenon raises an important question for LRM self-evaluation: How can a model autonomously assess the correctness of its own reasoning trajectory without external labels? To address this, we propose Chain-of-Reasoning Embedding (CoRE), a series of hidden states in latent space to enable label-free self-evaluation on intermediate reasoning steps of LRMs, so as to enhance metacognition abilities for improved reasoning efficiency. By analyzing the geometric properties of the CoRE trajectories, we reveal that redundant reasoning usually presents cyclical fluctuations, which correspond to repetitive and unconscious reflection/exploration. Leveraging this insight, we further introduce a training-free, label-free self-evaluation framework, CoRE-Eval, to detect such patterns and dynamically determine whether to terminate reasoning early. Extensive experiments on mathematical reasoning benchmarks (GSM8K, MATH-500, and AIME) and across model sizes from 7B to 32B demonstrate that CoRE-Eval reduces chain-of-thought length by 13.7% to 33.2% while improving answer accuracy by around 10%, achieving 70.0% accuracy on the challenging AIME benchmark with the 32B model.
| null |
https://arxiv.org/abs/2507.06087v1
|
https://arxiv.org/pdf/2507.06087v1.pdf
| null |
[
"Haoxi Li",
"Sikai Bai",
"Jie Zhang",
"Song Guo"
] |
[
"GSM8K",
"Math",
"Mathematical Reasoning",
"Program Synthesis"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/entropy-memorization-law-evaluating
|
2507.06056
| null | null |
Entropy-Memorization Law: Evaluating Memorization Difficulty of Data in LLMs
|
Large Language Models (LLMs) are known to memorize portions of their training data, sometimes reproducing content verbatim when prompted appropriately. In this work, we investigate a fundamental yet under-explored question in the domain of memorization: How to characterize memorization difficulty of training data in LLMs? Through empirical experiments on OLMo, a family of open models, we present the Entropy-Memorization Law. It suggests that data entropy is linearly correlated with memorization score. Moreover, in a case study of memorizing highly randomized strings, or "gibberish", we observe that such sequences, despite their apparent randomness, exhibit unexpectedly low empirical entropy compared to the broader training corpus. Adopting the same strategy to discover Entropy-Memorization Law, we derive a simple yet effective approach to distinguish training and testing data, enabling Dataset Inference (DI).
| null |
https://arxiv.org/abs/2507.06056v1
|
https://arxiv.org/pdf/2507.06056v1.pdf
| null |
[
"Yizhan Huang",
"Zhe Yang",
"Meifang Chen",
"Jianping Zhang",
"Michael R. Lyu"
] |
[
"Memorization"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/kamae-bridging-spark-and-keras-for-seamless
|
2507.06021
| null | null |
Kamae: Bridging Spark and Keras for Seamless ML Preprocessing
|
In production recommender systems, feature preprocessing must be faithfully replicated across training and inference environments. This often requires duplicating logic between offline and online environments, increasing engineering effort and introducing risks of dataset shift. We present Kamae, an open-source Python library that bridges this gap by translating PySpark preprocessing pipelines into equivalent Keras models. Kamae provides a suite of configurable Spark transformers and estimators, each mapped to a corresponding Keras layer, enabling consistent, end-to-end preprocessing across the ML lifecycle. Framework's utility is illustrated on real-world use cases, including MovieLens dataset and Expedia's Learning-to-Rank pipelines. The code is available at https://github.com/ExpediaGroup/kamae.
|
In production recommender systems, feature preprocessing must be faithfully replicated across training and inference environments.
|
https://arxiv.org/abs/2507.06021v1
|
https://arxiv.org/pdf/2507.06021v1.pdf
| null |
[
"George Barrowclough",
"Marian Andrecki",
"James Shinner",
"Daniele Donghi"
] |
[
"Learning-To-Rank",
"Recommendation Systems"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/docie-xllm25-in-context-learning-for
|
2507.05997
| null | null |
DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations
|
Large, high-quality annotated corpora remain scarce in document-level entity and relation extraction in zero-shot or few-shot settings. In this paper, we present a fully automatic, LLM-based pipeline for synthetic data generation and in-context learning for document-level entity and relation extraction. In contrast to existing approaches that rely on manually annotated demonstrations or direct zero-shot inference, our method combines synthetic data generation with retrieval-based in-context learning, using a reasoning-optimized language model. This allows us to build a high-quality demonstration database without manual annotation and to dynamically retrieve relevant examples at inference time. Based on our approach we produce a synthetic dataset of over $5k$ Wikipedia abstracts with approximately $59k$ entities and $30k$ relation triples. Finally, we evaluate in-context learning performance on the DocIE shared task, extracting entities and relations from long documents in a zero-shot setting. We find that in-context joint entity and relation extraction at document-level remains a challenging task, even for state-of-the-art large language models.
| null |
https://arxiv.org/abs/2507.05997v1
|
https://arxiv.org/pdf/2507.05997v1.pdf
| null |
[
"Nicholas Popovič",
"Ashish Kangen",
"Tim Schopf",
"Michael Färber"
] |
[
"In-Context Learning",
"Joint Entity and Relation Extraction",
"Relation",
"Relation Extraction",
"Synthetic Data Generation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/best-of-n-through-the-smoothing-lens-kl
|
2507.05913
| null | null |
Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis
|
A simple yet effective method for inference-time alignment of generative models is Best-of-$N$ (BoN), where $N$ outcomes are sampled from a reference policy, evaluated using a proxy reward model, and the highest-scoring one is selected. While prior work argues that BoN is almost optimal in reward vs KL tradeoffs, the effectiveness of BoN depends critically on the quality of the proxy reward model used for selection. For this purpose, we study BoN through a smooth version known as Soft Best-of-N (SBoN) and develop a theoretical framework to address this gap. We analyze the scaling behaviour of BoN by providing bounds on the KL divergence between the SBoN policy and the reference policy, offering insights into how performance varies with the number of samples. We also study the regret gap, i.e., the gap between the expected true reward under the optimal policy and the SBoN policy. Our theoretical and empirical findings show that smoothing helps SBoN mitigate reward overoptimization, especially when the quality of the proxy reward is low.
| null |
https://arxiv.org/abs/2507.05913v1
|
https://arxiv.org/pdf/2507.05913v1.pdf
| null |
[
"Gholamali Aminian",
"Idan Shenfeld",
"Amir R. Asadi",
"Ahmad Beirami",
"Youssef Mroueh"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/decomposing-the-time-series-forecasting
|
2507.05891
| null | null |
Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection
|
With the advent of Transformers, time series forecasting has seen significant advances, yet it remains challenging due to the need for effective sequence representation, memory construction, and accurate target projection. Time series forecasting remains a challenging task, demanding effective sequence representation, meaningful information extraction, and precise future projection. Each dataset and forecasting configuration constitutes a distinct task, each posing unique challenges the model must overcome to produce accurate predictions. To systematically address these task-specific difficulties, this work decomposes the time series forecasting pipeline into three core stages: input sequence representation, information extraction and memory construction, and final target projection. Within each stage, we investigate a range of architectural configurations to assess the effectiveness of various modules, such as convolutional layers for feature extraction and self-attention mechanisms for information extraction, across diverse forecasting tasks, including evaluations on seven benchmark datasets. Our models achieve state-of-the-art forecasting accuracy while greatly enhancing computational efficiency, with reduced training and inference times and a lower parameter count. The source code is available at https://github.com/RobertLeppich/REP-Net.
|
With the advent of Transformers, time series forecasting has seen significant advances, yet it remains challenging due to the need for effective sequence representation, memory construction, and accurate target projection.
|
https://arxiv.org/abs/2507.05891v1
|
https://arxiv.org/pdf/2507.05891v1.pdf
| null |
[
"Robert Leppich",
"Michael Stenger",
"André Bauer",
"Samuel Kounev"
] |
[
"Computational Efficiency",
"Time Series",
"Time Series Forecasting"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/automatic-synthesis-of-high-quality-triplet
|
2507.05970
| null | null |
Automatic Synthesis of High-Quality Triplet Data for Composed Image Retrieval
|
As a challenging vision-language (VL) task, Composed Image Retrieval (CIR) aims to retrieve target images using multimodal (image+text) queries. Although many existing CIR methods have attained promising performance, their reliance on costly, manually labeled triplets hinders scalability and zero-shot capability. To address this issue, we propose a scalable pipeline for automatic triplet generation, along with a fully synthetic dataset named Composed Image Retrieval on High-quality Synthetic Triplets (CIRHS). Our pipeline leverages a large language model (LLM) to generate diverse prompts, controlling a text-to-image generative model to produce image pairs with identical elements in each pair, which are then filtered and reorganized to form the CIRHS dataset. In addition, we introduce Hybrid Contextual Alignment (CoAlign), a novel CIR framework, which can accomplish global alignment and local reasoning within a broader context, enabling the model to learn more robust and informative representations. By utilizing the synthetic CIRHS dataset, CoAlign achieves outstanding zero-shot performance on three commonly used benchmarks, demonstrating for the first time the feasibility of training CIR models on a fully synthetic dataset. Furthermore, under supervised training, our method outperforms all the state-of-the-art supervised CIR approaches, validating the effectiveness of our proposed retrieval framework. The code and the CIRHS dataset will be released soon.
| null |
https://arxiv.org/abs/2507.05970v1
|
https://arxiv.org/pdf/2507.05970v1.pdf
| null |
[
"Haiwen Li",
"Delong Liu",
"Zhaohui Hou",
"Zhicheng Zhao",
"Fei Su"
] |
[
"Image Retrieval",
"Large Language Model",
"Retrieval",
"Triplet"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/high-fidelity-and-generalizable-neural
|
2507.05952
| null | null |
High-Fidelity and Generalizable Neural Surface Reconstruction with Sparse Feature Volumes
|
Generalizable neural surface reconstruction has become a compelling technique to reconstruct from few images without per-scene optimization, where dense 3D feature volume has proven effective as a global representation of scenes. However, the dense representation does not scale well to increasing voxel resolutions, severely limiting the reconstruction quality. We thus present a sparse representation method, that maximizes memory efficiency and enables significantly higher resolution reconstructions on standard hardware. We implement this through a two-stage approach: First training a network to predict voxel occupancies from posed images and associated depth maps, then computing features and performing volume rendering only in voxels with sufficiently high occupancy estimates. To support this sparse representation, we developed custom algorithms for efficient sampling, feature aggregation, and querying from sparse volumes-overcoming the dense-volume assumptions inherent in existing works. Experiments on public datasets demonstrate that our approach reduces storage requirements by more than 50 times without performance degradation, enabling reconstructions at $512^3$ resolution compared to the typical $128^3$ on similar hardware, and achieving superior reconstruction accuracy over current state-of-the-art methods.
| null |
https://arxiv.org/abs/2507.05952v1
|
https://arxiv.org/pdf/2507.05952v1.pdf
| null |
[
"Aoxiang Fan",
"Corentin Dumery",
"Nicolas Talabot",
"Hieu Le",
"Pascal Fua"
] |
[
"Surface Reconstruction"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/tigaug-data-augmentation-for-testing-traffic
|
2507.05932
| null | null |
TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems
|
Autonomous vehicle technology has been developed in the last decades with recent advances in sensing and computing technology. There is an urgent need to ensure the reliability and robustness of autonomous driving systems (ADSs). Despite the recent achievements in testing various ADS modules, little attention has been paid on the automated testing of traffic light detection models in ADSs. A common practice is to manually collect and label traffic light data. However, it is labor-intensive, and even impossible to collect diverse data under different driving environments. To address these problems, we propose and implement TigAug to automatically augment labeled traffic light images for testing traffic light detection models in ADSs. We construct two families of metamorphic relations and three families of transformations based on a systematic understanding of weather environments, camera properties, and traffic light properties. We use augmented images to detect erroneous behaviors of traffic light detection models by transformation-specific metamorphic relations, and to improve the performance of traffic light detection models by retraining. Large-scale experiments with four state-of-the-art traffic light detection models and two traffic light datasets have demonstrated that i) TigAug is effective in testing traffic light detection models, ii) TigAug is efficient in synthesizing traffic light images, and iii) TigAug generates traffic light images with acceptable naturalness.
| null |
https://arxiv.org/abs/2507.05932v1
|
https://arxiv.org/pdf/2507.05932v1.pdf
| null |
[
"You Lu",
"Dingji Wang",
"Kaifeng Huang",
"Bihuan Chen",
"Xin Peng"
] |
[
"Autonomous Driving",
"Data Augmentation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/aligned-textual-scoring-rules
|
2507.06221
| null | null |
Aligned Textual Scoring Rules
|
Scoring rules elicit probabilistic predictions from a strategic agent by scoring the prediction against a ground truth state. A scoring rule is proper if, from the agent's perspective, reporting the true belief maximizes the expected score. With the development of language models, Wu and Hartline (2024) proposes a reduction from textual information elicitation to the numerical (i.e. probabilistic) information elicitation problem, which achieves provable properness for textual elicitation. However, not all proper scoring rules are well aligned with human preference over text. Our paper designs the Aligned Scoring rule (ASR) for text by optimizing and minimizing the mean squared error between a proper scoring rule and a reference score (e.g. human score). Our experiments show that our ASR outperforms previous methods in aligning with human preference while maintaining properness.
| null |
https://arxiv.org/abs/2507.06221v1
|
https://arxiv.org/pdf/2507.06221v1.pdf
| null |
[
"Yuxuan Lu",
"Yifan Wu",
"Jason Hartline",
"Michael J. Curry"
] |
[
"scoring rule"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/identifiability-in-causal-abstractions-a
|
2507.06213
| null | null |
Identifiability in Causal Abstractions: A Hierarchy of Criteria
|
Identifying the effect of a treatment from observational data typically requires assuming a fully specified causal diagram. However, such diagrams are rarely known in practice, especially in complex or high-dimensional settings. To overcome this limitation, recent works have explored the use of causal abstractions-simplified representations that retain partial causal information. In this paper, we consider causal abstractions formalized as collections of causal diagrams, and focus on the identifiability of causal queries within such collections. We introduce and formalize several identifiability criteria under this setting. Our main contribution is to organize these criteria into a structured hierarchy, highlighting their relationships. This hierarchical view enables a clearer understanding of what can be identified under varying levels of causal knowledge. We illustrate our framework through examples from the literature and provide tools to reason about identifiability when full causal knowledge is unavailable.
| null |
https://arxiv.org/abs/2507.06213v1
|
https://arxiv.org/pdf/2507.06213v1.pdf
| null |
[
"Clément Yvernes",
"Emilie Devijver",
"Marianne Clausel",
"Eric Gaussier"
] |
[] | 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/simultaneous-localization-and-mapping-using
|
2507.04662
| null | null |
Simultaneous Localization and Mapping Using Active mmWave Sensing in 5G NR
|
Millimeter-wave (mmWave) 5G New Radio (NR) communication systems, with their high-resolution antenna arrays and extensive bandwidth, offer a transformative opportunity for high-throughput data transmission and advanced environmental sensing. Although passive sensing-based SLAM techniques can estimate user locations and environmental reflections simultaneously, their effectiveness is often constrained by assumptions of specular reflections and oversimplified map representations. To overcome these limitations, this work employs a mmWave 5G NR system for active sensing, enabling it to function similarly to a laser scanner for point cloud generation. Specifically, point clouds are extracted from the power delay profile estimated from each beam direction using a binary search approach. To ensure accuracy, hardware delays are calibrated with multiple predefined target points. Pose variations of the terminal are then estimated from point cloud data gathered along continuous trajectory viewpoints using point cloud registration algorithms. Loop closure detection and pose graph optimization are subsequently applied to refine the sensing results, achieving precise terminal localization and detailed radio map reconstruction. The system is implemented and validated through both simulations and experiments, confirming the effectiveness of the proposed approach.
| null |
https://arxiv.org/abs/2507.04662v1
|
https://arxiv.org/pdf/2507.04662v1.pdf
| null |
[
"Tao Du",
"Jie Yang",
"Fan Liu",
"Jiaxiang Guo",
"Shuqiang Xia",
"Chao-Kai Wen",
"Shi Jin"
] |
[
"Loop Closure Detection",
"Point Cloud Generation",
"Point Cloud Registration",
"Simultaneous Localization and Mapping"
] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ragnnarok-a-light-weight-graph-neural-network
|
2507.00937
| null | null |
RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles
|
Low-cost indoor mobile robots have gained popularity with the increasing adoption of automation in homes and commercial spaces. However, existing lidar and camera-based solutions have limitations such as poor performance in visually obscured environments, high computational overhead for data processing, and high costs for lidars. In contrast, mmWave radar sensors offer a cost-effective and lightweight alternative, providing accurate ranging regardless of visibility. However, existing radar-based localization suffers from sparse point cloud generation, noise, and false detections. Thus, in this work, we introduce RaGNNarok, a real-time, lightweight, and generalizable graph neural network (GNN)-based framework to enhance radar point clouds, even in complex and dynamic environments. With an inference time of just 7.3 ms on the low-cost Raspberry Pi 5, RaGNNarok runs efficiently even on such resource-constrained devices, requiring no additional computational resources. We evaluate its performance across key tasks, including localization, SLAM, and autonomous navigation, in three different environments. Our results demonstrate strong reliability and generalizability, making RaGNNarok a robust solution for low-cost indoor mobile robots.
| null |
https://arxiv.org/abs/2507.00937v1
|
https://arxiv.org/pdf/2507.00937v1.pdf
| null |
[
"David Hunt",
"Shaocheng Luo",
"Spencer Hallyburton",
"Shafii Nillongo",
"Yi Li",
"Tingjun Chen",
"Miroslav Pajic"
] |
[
"Autonomous Navigation",
"Graph Neural Network",
"Point Cloud Generation",
"Raspberry Pi 5"
] | 2025-07-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Graph Neural Network",
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "Graph Neural Network",
"source_title": "Graph Neural Networks: A Review of Methods and Applications",
"source_url": "https://arxiv.org/abs/1812.08434v6"
}
] |
https://paperswithcode.com/paper/adaptive-multipath-based-slam-for-distributed
|
2506.21798
| null | null |
Adaptive Multipath-Based SLAM for Distributed MIMO Systems
|
Localizing users and mapping the environment using radio signals is a key task in emerging applications such as reliable communications, location-aware security, and safety critical navigation. Recently introduced multipath-based simultaneous localization and mapping (MP-SLAM) can jointly localize a mobile agent and the reflective surfaces in radio frequency (RF) environments. Most existing MP-SLAM methods assume that map features and their corresponding RF propagation paths are statistically independent, which neglects inherent dependencies arising when a single reflective surface contributes to different propagation paths or when an agent communicates with more than one base station. Previous approaches that aim to fuse information across propagation paths are limited by their inability to perform ray tracing in environments with nonconvex geometries. In this paper, we propose a Bayesian MP-SLAM method for distributed MIMO systems that addresses this limitation. In particular, we use amplitude statistics to establish adaptive time-varying detection probabilities. Based on the resulting "soft" ray-tracing strategy, our method can fuse information across propagation paths in RF environments with nonconvex geometries. A Bayesian estimation method for the joint estimation of map features and agent position is established by applying the message passing rules of the sum-product algorithm (SPA) to the factor graph that represents the proposed statistical model. We also introduce an improved proposal PDF for particle-based computation of SPA messages. This proposal PDF enables the early detection of new surfaces that are solely supported by double-bounce paths. Our method is validated using synthetic RF measurements in a challenging scenario with nonconvex geometries. The results demonstrate that it can provide accurate localization and mapping estimates as well as attain the posterior CRLB.
| null |
https://arxiv.org/abs/2506.21798v1
|
https://arxiv.org/pdf/2506.21798v1.pdf
| null |
[
"Xuhong LI",
"Benjamin J. B. Deutschmann",
"Erik Leitinger",
"Florian Meyer"
] |
[
"Simultaneous Localization and Mapping"
] | 2025-06-26T00:00:00 | 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/ark-an-open-source-python-based-framework-for
|
2506.21628
| null | null |
Ark: An Open-source Python-based Framework for Robot Learning
|
Robotics has made remarkable hardware strides-from DARPA's Urban and Robotics Challenges to the first humanoid-robot kickboxing tournament-yet commercial autonomy still lags behind progress in machine learning. A major bottleneck is software: current robot stacks demand steep learning curves, low-level C/C++ expertise, fragmented tooling, and intricate hardware integration, in stark contrast to the Python-centric, well-documented ecosystems that propelled modern AI. We introduce ARK, an open-source, Python-first robotics framework designed to close that gap. ARK presents a Gym-style environment interface that allows users to collect data, preprocess it, and train policies using state-of-the-art imitation-learning algorithms (e.g., ACT, Diffusion Policy) while seamlessly toggling between high-fidelity simulation and physical robots. A lightweight client-server architecture provides networked publisher-subscriber communication, and optional C/C++ bindings ensure real-time performance when needed. ARK ships with reusable modules for control, SLAM, motion planning, system identification, and visualization, along with native ROS interoperability. Comprehensive documentation and case studies-from manipulation to mobile navigation-demonstrate rapid prototyping, effortless hardware swapping, and end-to-end pipelines that rival the convenience of mainstream machine-learning workflows. By unifying robotics and AI practices under a common Python umbrella, ARK lowers entry barriers and accelerates research and commercial deployment of autonomous robots.
| null |
https://arxiv.org/abs/2506.21628v1
|
https://arxiv.org/pdf/2506.21628v1.pdf
| null |
[
"Magnus Dierking",
"Christopher E. Mower",
"Sarthak Das",
"Huang Helong",
"Jiacheng Qiu",
"Cody Reading",
"Wei Chen",
"Huidong Liang",
"Huang Guowei",
"Jan Peters",
"Quan Xingyue",
"Jun Wang",
"Haitham Bou-Ammar"
] |
[
"Imitation Learning",
"Motion Planning"
] | 2025-06-24T00:00:00 | 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/feature-based-vs-gan-based-learning-from
|
2507.05906
| null | null |
Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why
|
This survey provides a comparative analysis of feature-based and GAN-based approaches to learning from demonstrations, with a focus on the structure of reward functions and their implications for policy learning. Feature-based methods offer dense, interpretable rewards that excel at high-fidelity motion imitation, yet often require sophisticated representations of references and struggle with generalization in unstructured settings. GAN-based methods, in contrast, use implicit, distributional supervision that enables scalability and adaptation flexibility, but are prone to training instability and coarse reward signals. Recent advancements in both paradigms converge on the importance of structured motion representations, which enable smoother transitions, controllable synthesis, and improved task integration. We argue that the dichotomy between feature-based and GAN-based methods is increasingly nuanced: rather than one paradigm dominating the other, the choice should be guided by task-specific priorities such as fidelity, diversity, interpretability, and adaptability. This work outlines the algorithmic trade-offs and design considerations that underlie method selection, offering a framework for principled decision-making in learning from demonstrations.
| null |
https://arxiv.org/abs/2507.05906v1
|
https://arxiv.org/pdf/2507.05906v1.pdf
| null |
[
"Chenhao Li",
"Marco Hutter",
"Andreas Krause"
] |
[
"Decision Making"
] | 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/comparison-of-path-planning-algorithms-for
|
2507.05884
| null | null |
Comparison of Path Planning Algorithms for Autonomous Vehicle Navigation Using Satellite and Airborne LiDAR Data
|
Autonomous vehicle navigation in unstructured environments, such as forests and mountainous regions, presents significant challenges due to irregular terrain and complex road conditions. This work provides a comparative evaluation of mainstream and well-established path planning algorithms applied to weighted pixel-level road networks derived from high-resolution satellite imagery and airborne LiDAR data. For 2D road-map navigation, where the weights reflect road conditions and terrain difficulty, A*, Dijkstra, RRT*, and a Novel Improved Ant Colony Optimization Algorithm (NIACO) are tested on the DeepGlobe satellite dataset. For 3D road-map path planning, 3D A*, 3D Dijkstra, RRT-Connect, and NIACO are evaluated using the Hamilton airborne LiDAR dataset, which provides detailed elevation information. All algorithms are assessed under identical start and end point conditions, focusing on path cost, computation time, and memory consumption. Results demonstrate that Dijkstra consistently offers the most stable and efficient performance in both 2D and 3D scenarios, particularly when operating on dense, pixel-level geospatial road-maps. These findings highlight the reliability of Dijkstra-based planning for static terrain navigation and establish a foundation for future research on dynamic path planning under complex environmental constraints.
|
For 3D road-map path planning, 3D A*, 3D Dijkstra, RRT-Connect, and NIACO are evaluated using the Hamilton airborne LiDAR dataset, which provides detailed elevation information.
|
https://arxiv.org/abs/2507.05884v1
|
https://arxiv.org/pdf/2507.05884v1.pdf
| null |
[
"Chang Liu",
"Zhexiong Xue",
"Tamas Sziranyi"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/assessing-linear-control-strategies-for-zero
|
2507.05867
| null | null |
Assessing Linear Control Strategies for Zero-Speed Fin Roll Damping
|
Roll stabilization is a critical aspect of ship motion control, particularly for vessels operating in low-speed or zero-speed conditions, where traditional hydrodynamic fins lose their effectiveness. In this paper, we consider a roll damping system, developed by Navis JSC, based on two actively controlled zero-speed fins. Unlike conventional fin stabilizers, zero-speed fins employ a drag-based mechanism and active oscillations to generate stabilizing forces even when the vessel is stationary. We propose a simple linear control architecture that, however, accounts for nonlinear drag forces and actuator limitations. Simulation results on a high-fidelity vessel model used for HIL testing demonstrate the effectiveness of the proposed approach.
| null |
https://arxiv.org/abs/2507.05867v1
|
https://arxiv.org/pdf/2507.05867v1.pdf
| null |
[
"Nikita Savin",
"Elena Ambrosovskaya",
"Dmitry Romaev",
"Anton Proskurnikov"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/video-event-reasoning-and-prediction-by
|
2507.05822
| null | null |
Video Event Reasoning and Prediction by Fusing World Knowledge from LLMs with Vision Foundation Models
|
Current video understanding models excel at recognizing "what" is happening but fall short in high-level cognitive tasks like causal reasoning and future prediction, a limitation rooted in their lack of commonsense world knowledge. To bridge this cognitive gap, we propose a novel framework that synergistically fuses a powerful Vision Foundation Model (VFM) for deep visual perception with a Large Language Model (LLM) serving as a knowledge-driven reasoning core. Our key technical innovation is a sophisticated fusion module, inspired by the Q-Former architecture, which distills complex spatiotemporal and object-centric visual features into a concise, language-aligned representation. This enables the LLM to effectively ground its inferential processes in direct visual evidence. The model is trained via a two-stage strategy, beginning with large-scale alignment pre-training on video-text data, followed by targeted instruction fine-tuning on a curated dataset designed to elicit advanced reasoning and prediction skills. Extensive experiments demonstrate that our model achieves state-of-the-art performance on multiple challenging benchmarks. Notably, it exhibits remarkable zero-shot generalization to unseen reasoning tasks, and our in-depth ablation studies validate the critical contribution of each architectural component. This work pushes the boundary of machine perception from simple recognition towards genuine cognitive understanding, paving the way for more intelligent and capable AI systems in robotics, human-computer interaction, and beyond.
| null |
https://arxiv.org/abs/2507.05822v1
|
https://arxiv.org/pdf/2507.05822v1.pdf
| null |
[
"L'ea Dubois",
"Klaus Schmidt",
"Chengyu Wang",
"Ji-Hoon Park",
"Lin Wang",
"Santiago Munoz"
] |
[
"Future prediction",
"Large Language Model",
"Video Understanding",
"World Knowledge",
"Zero-shot Generalization"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/3dgs-lsr-large-scale-relocation-for
|
2507.05661
| null | null |
3DGS_LSR:Large_Scale Relocation for Autonomous Driving Based on 3D Gaussian Splatting
|
In autonomous robotic systems, precise localization is a prerequisite for safe navigation. However, in complex urban environments, GNSS positioning often suffers from signal occlusion and multipath effects, leading to unreliable absolute positioning. Traditional mapping approaches are constrained by storage requirements and computational inefficiency, limiting their applicability to resource-constrained robotic platforms. To address these challenges, we propose 3DGS-LSR: a large-scale relocalization framework leveraging 3D Gaussian Splatting (3DGS), enabling centimeter-level positioning using only a single monocular RGB image on the client side. We combine multi-sensor data to construct high-accuracy 3DGS maps in large outdoor scenes, while the robot-side localization requires just a standard camera input. Using SuperPoint and SuperGlue for feature extraction and matching, our core innovation is an iterative optimization strategy that refines localization results through step-by-step rendering, making it suitable for real-time autonomous navigation. Experimental validation on the KITTI dataset demonstrates our 3DGS-LSR achieves average positioning accuracies of 0.026m, 0.029m, and 0.081m in town roads, boulevard roads, and traffic-dense highways respectively, significantly outperforming other representative methods while requiring only monocular RGB input. This approach provides autonomous robots with reliable localization capabilities even in challenging urban environments where GNSS fails.
| null |
https://arxiv.org/abs/2507.05661v1
|
https://arxiv.org/pdf/2507.05661v1.pdf
| null |
[
"Haitao Lu",
"Haijier Chen",
"Haoze Liu",
"Shoujian Zhang",
"Bo Xu",
"Ziao Liu"
] |
[
"3DGS",
"Autonomous Driving",
"Autonomous Navigation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dreamgrasp-zero-shot-3d-multi-object
|
2507.05627
| null | null |
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
|
Partial-view 3D recognition -- reconstructing 3D geometry and identifying object instances from a few sparse RGB images -- is an exceptionally challenging yet practically essential task, particularly in cluttered, occluded real-world settings where full-view or reliable depth data are often unavailable. Existing methods, whether based on strong symmetry priors or supervised learning on curated datasets, fail to generalize to such scenarios. In this work, we introduce DreamGrasp, a framework that leverages the imagination capability of large-scale pre-trained image generative models to infer the unobserved parts of a scene. By combining coarse 3D reconstruction, instance segmentation via contrastive learning, and text-guided instance-wise refinement, DreamGrasp circumvents limitations of prior methods and enables robust 3D reconstruction in complex, multi-object environments. Our experiments show that DreamGrasp not only recovers accurate object geometry but also supports downstream tasks like sequential decluttering and target retrieval with high success rates.
| null |
https://arxiv.org/abs/2507.05627v1
|
https://arxiv.org/pdf/2507.05627v1.pdf
| null |
[
"Young Hun Kim",
"Seungyeon Kim",
"Yonghyeon LEE",
"Frank Chongwoo Park"
] |
[
"3D geometry",
"3D Reconstruction",
"Contrastive Learning",
"Instance Segmentation",
"Object",
"Object Reconstruction",
"Semantic Segmentation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/detecting-and-mitigating-reward-hacking-in
|
2507.05619
| null | null |
Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study
|
Reward hacking in Reinforcement Learning (RL) systems poses a critical threat to the deployment of autonomous agents, where agents exploit flaws in reward functions to achieve high scores without fulfilling intended objectives. Despite growing awareness of this problem, systematic detection and mitigation approaches remain limited. This paper presents a large-scale empirical study of reward hacking across diverse RL environments and algorithms. We analyze 15,247 training episodes across 15 RL environments (Atari, MuJoCo, custom domains) and 5 algorithms (PPO, SAC, DQN, A3C, Rainbow), implementing automated detection algorithms for six categories of reward hacking: specification gaming, reward tampering, proxy optimization, objective misalignment, exploitation patterns, and wireheading. Our detection framework achieves 78.4% precision and 81.7% recall across environments, with computational overhead under 5%. Through controlled experiments varying reward function properties, we demonstrate that reward density and alignment with true objectives significantly impact hacking frequency ($p < 0.001$, Cohen's $d = 1.24$). We validate our approach through three simulated application studies representing recommendation systems, competitive gaming, and robotic control scenarios. Our mitigation techniques reduce hacking frequency by up to 54.6% in controlled scenarios, though we find these trade-offs are more challenging in practice due to concept drift, false positive costs, and adversarial adaptation. All detection algorithms, datasets, and experimental protocols are publicly available to support reproducible research in RL safety.
| null |
https://arxiv.org/abs/2507.05619v1
|
https://arxiv.org/pdf/2507.05619v1.pdf
| null |
[
"Ibne Farabi Shihab",
"Sanjeda Akter",
"Anuj Sharma"
] |
[
"MuJoCo",
"Recommendation Systems",
"Reinforcement Learning (RL)"
] | 2025-07-08T00:00:00 | null | null | null | 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": "**A3C**, **Asynchronous Advantage Actor Critic**, is a policy gradient algorithm in reinforcement learning that maintains a policy $\\pi\\left(a\\_{t}\\mid{s}\\_{t}; \\theta\\right)$ and an estimate of the value\r\nfunction $V\\left(s\\_{t}; \\theta\\_{v}\\right)$. It operates in the forward view and uses a mix of $n$-step returns to update both the policy and the value-function. The policy and the value function are updated after every $t\\_{\\text{max}}$ actions or when a terminal state is reached. The update performed by the algorithm can be seen as $\\nabla\\_{\\theta{'}}\\log\\pi\\left(a\\_{t}\\mid{s\\_{t}}; \\theta{'}\\right)A\\left(s\\_{t}, a\\_{t}; \\theta, \\theta\\_{v}\\right)$ where $A\\left(s\\_{t}, a\\_{t}; \\theta, \\theta\\_{v}\\right)$ is an estimate of the advantage function given by:\r\n\r\n$$\\sum^{k-1}\\_{i=0}\\gamma^{i}r\\_{t+i} + \\gamma^{k}V\\left(s\\_{t+k}; \\theta\\_{v}\\right) - V\\left(s\\_{t}; \\theta\\_{v}\\right)$$\r\n\r\nwhere $k$ can vary from state to state and is upper-bounded by $t\\_{max}$.\r\n\r\nThe critics in A3C learn the value function while multiple actors are trained in parallel and get synced with global parameters every so often. The gradients are accumulated as part of training for stability - this is like parallelized stochastic gradient descent.\r\n\r\nNote that while the parameters $\\theta$ of the policy and $\\theta\\_{v}$ of the value function are shown as being separate for generality, we always share some of the parameters in practice. We typically use a convolutional neural network that has one [softmax](https://paperswithcode.com/method/softmax) output for the policy $\\pi\\left(a\\_{t}\\mid{s}\\_{t}; \\theta\\right)$ and one linear output for the value function $V\\left(s\\_{t}; \\theta\\_{v}\\right)$, with all non-output layers shared.",
"full_name": "A3C",
"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": "A3C",
"source_title": "Asynchronous Methods for Deep Reinforcement Learning",
"source_url": "http://arxiv.org/abs/1602.01783v2"
},
{
"code_snippet_url": null,
"description": "**Q-Learning** is an off-policy temporal difference control algorithm:\r\n\r\n$$Q\\left(S\\_{t}, A\\_{t}\\right) \\leftarrow Q\\left(S\\_{t}, A\\_{t}\\right) + \\alpha\\left[R_{t+1} + \\gamma\\max\\_{a}Q\\left(S\\_{t+1}, a\\right) - Q\\left(S\\_{t}, A\\_{t}\\right)\\right] $$\r\n\r\nThe learned action-value function $Q$ directly approximates $q\\_{*}$, the optimal action-value function, independent of the policy being followed.\r\n\r\nSource: Sutton and Barto, Reinforcement Learning, 2nd Edition",
"full_name": "Q-Learning",
"introduced_year": 1984,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Off-Policy TD Control",
"parent": null
},
"name": "Q-Learning",
"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": "**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": "A **DQN**, or Deep Q-Network, approximates a state-value function in a [Q-Learning](https://paperswithcode.com/method/q-learning) framework with a neural network. In the Atari Games case, they take in several frames of the game as an input and output state values for each action as an output. \r\n\r\nIt is usually used in conjunction with [Experience Replay](https://paperswithcode.com/method/experience-replay), for storing the episode steps in memory for off-policy learning, where samples are drawn from the replay memory at random. Additionally, the Q-Network is usually optimized towards a frozen target network that is periodically updated with the latest weights every $k$ steps (where $k$ is a hyperparameter). The latter makes training more stable by preventing short-term oscillations from a moving target. The former tackles autocorrelation that would occur from on-line learning, and having a replay memory makes the problem more like a supervised learning problem.\r\n\r\nImage Source: [here](https://www.researchgate.net/publication/319643003_Autonomous_Quadrotor_Landing_using_Deep_Reinforcement_Learning)",
"full_name": "Deep Q-Network",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Q-Learning Networks",
"parent": "Off-Policy TD Control"
},
"name": "DQN",
"source_title": "Playing Atari with Deep Reinforcement Learning",
"source_url": "http://arxiv.org/abs/1312.5602v1"
}
] |
https://paperswithcode.com/paper/towards-solar-altitude-guided-scene
|
2507.05812
| null | null |
Towards Solar Altitude Guided Scene Illumination
|
The development of safe and robust autonomous driving functions is heavily dependent on large-scale, high-quality sensor data. However, real-word data acquisition demands intensive human labor and is strongly limited by factors such as labeling cost, driver safety protocols and diverse scenario coverage. Thus, multiple lines of work focus on the conditional generation of synthetic camera sensor data. We identify a significant gap in research regarding daytime variation, presumably caused by the scarcity of available labels. Consequently, we present the solar altitude as global conditioning variable. It is readily computable from latitude-longitude coordinates and local time, eliminating the need for extensive manual labeling. Our work is complemented by a tailored normalization approach, targeting the sensitivity of daylight towards small numeric changes in altitude. We demonstrate its ability to accurately capture lighting characteristics and illumination-dependent image noise in the context of diffusion models.
| null |
https://arxiv.org/abs/2507.05812v1
|
https://arxiv.org/pdf/2507.05812v1.pdf
| null |
[
"Samed Doğan",
"Maximilian Hoh",
"Nico Leuze",
"Nicolas R. -Peña",
"Alfred Schöttl"
] |
[
"Autonomous Driving"
] | 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/senseshift6d-multimodal-rgb-d-benchmarking
|
2507.05751
| null | null |
SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations
|
Recent advances on 6D object-pose estimation has achieved high performance on representative benchmarks such as LM-O, YCB-V, and T-Less. However, these datasets were captured under fixed illumination and camera settings, leaving the impact of real-world variations in illumination, exposure, gain or depth-sensor mode - and the potential of test-time sensor control to mitigate such variations - largely unexplored. To bridge this gap, we introduce SenseShift6D, the first RGB-D dataset that physically sweeps 13 RGB exposures, 9 RGB gains, auto-exposure, 4 depth-capture modes, and 5 illumination levels. For three common household objects (spray, pringles, and tincase), we acquire 101.9k RGB and 10k depth images, which can provide 1,380 unique sensor-lighting permutations per object pose. Experiments with state-of-the-art models on our dataset show that applying sensor control during test-time induces greater performance improvement over digital data augmentation, achieving performance comparable to or better than costly increases in real-world training data quantity and diversity. Adapting either RGB or depth sensors individually is effective, while jointly adapting multimodal RGB-D configurations yields even greater improvements. SenseShift6D extends the 6D-pose evaluation paradigm from data-centered to sensor-aware robustness, laying a foundation for adaptive, self-tuning perception systems capable of operating robustly in uncertain real-world environments. Our dataset is available at: huggingface.co/datasets/Yegyu/SenseShift6D Associated scripts can be found at: github.com/yegyu-han/SenseShift6D
|
Recent advances on 6D object-pose estimation has achieved high performance on representative benchmarks such as LM-O, YCB-V, and T-Less.
|
https://arxiv.org/abs/2507.05751v1
|
https://arxiv.org/pdf/2507.05751v1.pdf
| null |
[
"Yegyu Han",
"Taegyoon Yoon",
"Dayeon Woo",
"Sojeong Kim",
"Hyung-Sin Kim"
] |
[
"6D Pose Estimation",
"6D Pose Estimation using RGB",
"Benchmarking",
"Data Augmentation",
"Pose Estimation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/lion-lora-rethinking-lora-fusion-to-unify
|
2507.05678
| null | null |
LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion
|
Video Diffusion Models (VDMs) have demonstrated remarkable capabilities in synthesizing realistic videos by learning from large-scale data. Although vanilla Low-Rank Adaptation (LoRA) can learn specific spatial or temporal movement to driven VDMs with constrained data, achieving precise control over both camera trajectories and object motion remains challenging due to the unstable fusion and non-linear scalability. To address these issues, we propose LiON-LoRA, a novel framework that rethinks LoRA fusion through three core principles: Linear scalability, Orthogonality, and Norm consistency. First, we analyze the orthogonality of LoRA features in shallow VDM layers, enabling decoupled low-level controllability. Second, norm consistency is enforced across layers to stabilize fusion during complex camera motion combinations. Third, a controllable token is integrated into the diffusion transformer (DiT) to linearly adjust motion amplitudes for both cameras and objects with a modified self-attention mechanism to ensure decoupled control. Additionally, we extend LiON-LoRA to temporal generation by leveraging static-camera videos, unifying spatial and temporal controllability. Experiments demonstrate that LiON-LoRA outperforms state-of-the-art methods in trajectory control accuracy and motion strength adjustment, achieving superior generalization with minimal training data. Project Page: https://fuchengsu.github.io/lionlora.github.io/
| null |
https://arxiv.org/abs/2507.05678v1
|
https://arxiv.org/pdf/2507.05678v1.pdf
| null |
[
"Yisu Zhang",
"Chenjie Cao",
"Chaohui Yu",
"Jianke Zhu"
] |
[] | 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/gsvr-2d-gaussian-based-video-representation
|
2507.05594
| null | null |
GSVR: 2D Gaussian-based Video Representation for 800+ FPS with Hybrid Deformation Field
|
Implicit neural representations for video have been recognized as a novel and promising form of video representation. Existing works pay more attention to improving video reconstruction quality but little attention to the decoding speed. However, the high computation of convolutional network used in existing methods leads to low decoding speed. Moreover, these convolution-based video representation methods also suffer from long training time, about 14 seconds per frame to achieve 35+ PSNR on Bunny. To solve the above problems, we propose GSVR, a novel 2D Gaussian-based video representation, which achieves 800+ FPS and 35+ PSNR on Bunny, only needing a training time of $2$ seconds per frame. Specifically, we propose a hybrid deformation field to model the dynamics of the video, which combines two motion patterns, namely the tri-plane motion and the polynomial motion, to deal with the coupling of camera motion and object motion in the video. Furthermore, we propose a Dynamic-aware Time Slicing strategy to adaptively divide the video into multiple groups of pictures(GOP) based on the dynamic level of the video in order to handle large camera motion and non-rigid movements. Finally, we propose quantization-aware fine-tuning to avoid performance reduction after quantization and utilize image codecs to compress Gaussians to achieve a compact representation. Experiments on the Bunny and UVG datasets confirm that our method converges much faster than existing methods and also has 10x faster decoding speed compared to other methods. Our method has comparable performance in the video interpolation task to SOTA and attains better video compression performance than NeRV.
| null |
https://arxiv.org/abs/2507.05594v1
|
https://arxiv.org/pdf/2507.05594v1.pdf
| null |
[
"Zhizhuo Pang",
"Zhihui Ke",
"Xiaobo Zhou",
"Tie Qiu"
] |
[
"Quantization",
"Video Compression",
"Video Reconstruction"
] | 2025-07-08T00:00:00 | 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/loomnet-enhancing-multi-view-image-generation
|
2507.05499
| null | null |
LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving
|
Generating consistent multi-view images from a single image remains challenging. Lack of spatial consistency often degrades 3D mesh quality in surface reconstruction. To address this, we propose LoomNet, a novel multi-view diffusion architecture that produces coherent images by applying the same diffusion model multiple times in parallel to collaboratively build and leverage a shared latent space for view consistency. Each viewpoint-specific inference generates an encoding representing its own hypothesis of the novel view from a given camera pose, which is projected onto three orthogonal planes. For each plane, encodings from all views are fused into a single aggregated plane. These aggregated planes are then processed to propagate information and interpolate missing regions, combining the hypotheses into a unified, coherent interpretation. The final latent space is then used to render consistent multi-view images. LoomNet generates 16 high-quality and coherent views in just 15 seconds. In our experiments, LoomNet outperforms state-of-the-art methods on both image quality and reconstruction metrics, also showing creativity by producing diverse, plausible novel views from the same input.
| null |
https://arxiv.org/abs/2507.05499v1
|
https://arxiv.org/pdf/2507.05499v1.pdf
| null |
[
"Giulio Federico",
"Fabio Carrara",
"Claudio Gennaro",
"Giuseppe Amato",
"Marco Di Benedetto"
] |
[
"Image Generation",
"Surface Reconstruction"
] | 2025-07-07T00:00:00 | 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/yolo-apd-enhancing-yolov8-for-robust
|
2507.05376
| null | null |
YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries
|
Autonomous vehicle perception systems require robust pedestrian detection, particularly on geometrically complex roadways like Type-S curved surfaces, where standard RGB camera-based methods face limitations. This paper introduces YOLO-APD, a novel deep learning architecture enhancing the YOLOv8 framework specifically for this challenge. YOLO-APD integrates several key architectural modifications: a parameter-free SimAM attention mechanism, computationally efficient C3Ghost modules, a novel SimSPPF module for enhanced multi-scale feature pooling, the Mish activation function for improved optimization, and an Intelligent Gather & Distribute (IGD) module for superior feature fusion in the network's neck. The concept of leveraging vehicle steering dynamics for adaptive region-of-interest processing is also presented. Comprehensive evaluations on a custom CARLA dataset simulating complex scenarios demonstrate that YOLO-APD achieves state-of-the-art detection accuracy, reaching 77.7% [email protected]:0.95 and exceptional pedestrian recall exceeding 96%, significantly outperforming baseline models, including YOLOv8. Furthermore, it maintains real-time processing capabilities at 100 FPS, showcasing a superior balance between accuracy and efficiency. Ablation studies validate the synergistic contribution of each integrated component. Evaluation on the KITTI dataset confirms the architecture's potential while highlighting the need for domain adaptation. This research advances the development of highly accurate, efficient, and adaptable perception systems based on cost-effective sensors, contributing to enhanced safety and reliability for autonomous navigation in challenging, less-structured driving environments.
| null |
https://arxiv.org/abs/2507.05376v1
|
https://arxiv.org/pdf/2507.05376v1.pdf
| null |
[
"Aquino Joctum",
"John Kandiri"
] |
[
"Autonomous Navigation",
"Domain Adaptation",
"Pedestrian Detection"
] | 2025-07-07T00:00:00 | 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": "How do I file a claim 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. Is Expedia actually fully refundable? Expedia 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. What is the refundable option on Expedia? The 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. How do I get a human on Expedia? To reach a human at Expedia, call + ( 1 ) ⟷ 888 ⟷ ( 829 ) ⟷ 0881 or + ( 1 ) ⟷ 805 ⟷ ( 330 ) ⟷ 4056 and follow the prompts to speak with a representative. For quicker service, have your booking info ready. Mention any issues during the call + ( 1 ) ⟷ 888 ⟷ ( 829 ) ⟷ 0881 or + ( 1 ) ⟷ 805 ⟷ ( 330 ) ⟷ 4056 —Expedia may offer exclusive discounts or travel credits as a goodwill gesture for your trouble. How do I get a human at Expedia? To get a human at Expedia, call + ( 1 ) ⟷ 888 ⟷ ( 829 ) ⟷ 0881 or + ( 1 ) ⟷ 805 ⟷ ( 330 ) ⟷ 4056 and say “agent” or “representative” when prompted. For faster assistance, keep your booking details handy call + ( 1 ) ⟷ 888 ⟷ ( 829 ) ⟷ 0881 or + ( 1 ) ⟷ 805 ⟷ ( 330 ) ⟷ 4056. Mention any inconvenience you’ve faced—Expedia might provide a special discount or travel credit as a goodwill offer to enhance your experience.",
"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"
},
{
"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": "",
"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"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "You Only Look Once",
"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": "YOLOv8",
"source_title": "YOLOv3: An Incremental Improvement",
"source_url": "http://arxiv.org/abs/1804.02767v1"
}
] |
https://paperswithcode.com/paper/beyond-one-shot-beyond-one-perspective-cross
|
2507.05260
| null | null |
Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
|
LiDAR representation learning aims to extract rich structural and semantic information from large-scale, readily available datasets, reducing reliance on costly human annotations. However, existing LiDAR representation strategies often overlook the inherent spatiotemporal cues in LiDAR sequences, limiting their effectiveness. In this work, we propose LiMA, a novel long-term image-to-LiDAR Memory Aggregation framework that explicitly captures longer range temporal correlations to enhance LiDAR representation learning. LiMA comprises three key components: 1) a Cross-View Aggregation module that aligns and fuses overlapping regions across neighboring camera views, constructing a more unified and redundancy-free memory bank; 2) a Long-Term Feature Propagation mechanism that efficiently aligns and integrates multi-frame image features, reinforcing temporal coherence during LiDAR representation learning; and 3) a Cross-Sequence Memory Alignment strategy that enforces consistency across driving sequences, improving generalization to unseen environments. LiMA maintains high pretraining efficiency and incurs no additional computational overhead during downstream tasks. Extensive experiments on mainstream LiDAR-based perception benchmarks demonstrate that LiMA significantly improves both LiDAR semantic segmentation and 3D object detection. We hope this work inspires more effective pretraining paradigms for autonomous driving. The code has be made publicly accessible for future research.
|
In this work, we propose LiMA, a novel long-term image-to-LiDAR Memory Aggregation framework that explicitly captures longer range temporal correlations to enhance LiDAR representation learning.
|
https://arxiv.org/abs/2507.05260v1
|
https://arxiv.org/pdf/2507.05260v1.pdf
| null |
[
"Xiang Xu",
"Lingdong Kong",
"Song Wang",
"Chuanwei Zhou",
"Qingshan Liu"
] |
[
"3D Object Detection",
"Autonomous Driving",
"LIDAR Semantic Segmentation",
"object-detection",
"Object Detection",
"Representation Learning",
"Semantic Segmentation"
] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/4dslomo-4d-reconstruction-for-high-speed
|
2507.05163
| null | null |
4DSloMo: 4D Reconstruction for High Speed Scene with Asynchronous Capture
|
Reconstructing fast-dynamic scenes from multi-view videos is crucial for high-speed motion analysis and realistic 4D reconstruction. However, the majority of 4D capture systems are limited to frame rates below 30 FPS (frames per second), and a direct 4D reconstruction of high-speed motion from low FPS input may lead to undesirable results. In this work, we propose a high-speed 4D capturing system only using low FPS cameras, through novel capturing and processing modules. On the capturing side, we propose an asynchronous capture scheme that increases the effective frame rate by staggering the start times of cameras. By grouping cameras and leveraging a base frame rate of 25 FPS, our method achieves an equivalent frame rate of 100-200 FPS without requiring specialized high-speed cameras. On processing side, we also propose a novel generative model to fix artifacts caused by 4D sparse-view reconstruction, as asynchrony reduces the number of viewpoints at each timestamp. Specifically, we propose to train a video-diffusion-based artifact-fix model for sparse 4D reconstruction, which refines missing details, maintains temporal consistency, and improves overall reconstruction quality. Experimental results demonstrate that our method significantly enhances high-speed 4D reconstruction compared to synchronous capture.
| null |
https://arxiv.org/abs/2507.05163v1
|
https://arxiv.org/pdf/2507.05163v1.pdf
| null |
[
"Yutian Chen",
"Shi Guo",
"Tianshuo Yang",
"Lihe Ding",
"Xiuyuan Yu",
"Jinwei Gu",
"Tianfan Xue"
] |
[
"4D reconstruction"
] | 2025-07-07T00:00:00 | 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/estimating-object-physical-properties-from-1
|
2507.05029
| null | null |
Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep Learning
|
Inertial mass plays a crucial role in robotic applications such as object grasping, manipulation, and simulation, providing a strong prior for planning and control. Accurately estimating an object's mass before interaction can significantly enhance the performance of various robotic tasks. However, mass estimation using only vision sensors is a relatively underexplored area. This paper proposes a novel approach combining sparse point-cloud data from depth images with RGB images to estimate the mass of objects. We evaluate a range of point-cloud processing architectures, alongside RGB-only methods. To overcome the limited availability of training data, we create a synthetic dataset using ShapeNetSem 3D models, simulating RGBD images via a Kinect camera. This synthetic data is used to train an image generation model for estimating dense depth maps, which we then use to augment an existing dataset of images paired with mass values. Our approach significantly outperforms existing benchmarks across all evaluated metrics. The data generation (https://github.com/RavineWindteer/ShapenetSem-to-RGBD) as well as the training of the depth estimator (https://github.com/RavineWindteer/GLPDepth-Edited) and the mass estimator (https://github.com/RavineWindteer/Depth-mass-estimator) are available online.
|
This synthetic data is used to train an image generation model for estimating dense depth maps, which we then use to augment an existing dataset of images paired with mass values.
|
https://arxiv.org/abs/2507.05029v1
|
https://arxiv.org/pdf/2507.05029v1.pdf
| null |
[
"Ricardo Cardoso",
"Plinio Moreno"
] |
[
"Image Generation"
] | 2025-07-07T00:00:00 |
https://arxiv.org/abs/2507.05029
|
https://arxiv.org/pdf/2507.05029
|
estimating-object-physical-properties-from
| null |
[] |
https://paperswithcode.com/paper/reloop-seeing-twice-and-thinking-backwards
|
2507.04943
| null | null |
ReLoop: "Seeing Twice and Thinking Backwards" via Closed-loop Training to Mitigate Hallucinations in Multimodal understanding
|
While Multimodal Large Language Models (MLLMs) have achieved remarkable progress in open-ended visual question answering, they remain vulnerable to hallucinations. These are outputs that contradict or misrepresent input semantics, posing a critical challenge to the reliability and factual consistency. Existing methods often rely on external verification or post-hoc correction, lacking an internal mechanism to validate outputs directly during training. To bridge this gap, we propose ReLoop, a unified closed-loop training framework that encourages multimodal consistency for cross-modal understanding in MLLMs. ReLoop adopts a ring-shaped structure that integrates three complementary consistency feedback mechanisms, obliging MLLMs to "seeing twice and thinking backwards". Specifically, ReLoop employs the frozen Consistency Feedback Plugin (CFP), comprising semantic reconstruction, visual description, and an attention supervision module for attention alignment. These components collectively enforce semantic reversibility, visual consistency, and interpretable attention, enabling the model to correct its outputs during training. Extensive evaluations and analyses demonstrate the effectiveness of ReLoop in reducing hallucination rates across multiple benchmarks, establishing a robust method for hallucination mitigation in MLLMs. We will release our source code and data in the camera-ready version.
| null |
https://arxiv.org/abs/2507.04943v1
|
https://arxiv.org/pdf/2507.04943v1.pdf
| null |
[
"Jianjiang Yang",
"Ziyan Huang",
"Yanshu Li"
] |
[
"Hallucination",
"Question Answering",
"Visual Question Answering"
] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/piggyback-camera-easy-to-deploy-visual
|
2507.04910
| null | null |
Piggyback Camera: Easy-to-Deploy Visual Surveillance by Mobile Sensing on Commercial Robot Vacuums
|
This paper presents Piggyback Camera, an easy-to-deploy system for visual surveillance using commercial robot vacuums. Rather than requiring access to internal robot systems, our approach mounts a smartphone equipped with a camera and Inertial Measurement Unit (IMU) on the robot, making it applicable to any commercial robot without hardware modifications. The system estimates robot poses through neural inertial navigation and efficiently captures images at regular spatial intervals throughout the cleaning task. We develop a novel test-time data augmentation method called Rotation-Augmented Ensemble (RAE) to mitigate domain gaps in neural inertial navigation. A loop closure method that exploits robot cleaning patterns further refines these estimated poses. We demonstrate the system with an object mapping application that analyzes captured images to geo-localize objects in the environment. Experimental evaluation in retail environments shows that our approach achieves 0.83 m relative pose error for robot localization and 0.97 m positional error for object mapping of over 100 items.
| null |
https://arxiv.org/abs/2507.04910v1
|
https://arxiv.org/pdf/2507.04910v1.pdf
| null |
[
"Ryo Yonetani"
] |
[
"Data Augmentation"
] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ugg-reid-uncertainty-guided-graph-model-for
|
2507.04638
| null | null |
UGG-ReID: Uncertainty-Guided Graph Model for Multi-Modal Object Re-Identification
|
Multi-modal object Re-IDentification (ReID) has gained considerable attention with the goal of retrieving specific targets across cameras using heterogeneous visual data sources. Existing methods primarily aim to improve identification performance, but often overlook the uncertainty arising from inherent defects, such as intra-modal noise and inter-modal conflicts. This uncertainty is particularly significant in the case of fine-grained local occlusion and frame loss, which becomes a challenge in multi-modal learning. To address the above challenge, we propose a robust approach named Uncertainty-Guided Graph model for multi-modal object ReID (UGG-ReID). UGG-ReID is designed to mitigate noise interference and facilitate effective multi-modal fusion by estimating both local and sample-level aleatoric uncertainty and explicitly modeling their dependencies. Specifically, we first propose the Gaussian patch-graph representation model that leverages uncertainty to quantify fine-grained local cues and capture their structural relationships. This process boosts the expressiveness of modal-specific information, ensuring that the generated embeddings are both more informative and robust. Subsequently, we design an uncertainty-guided mixture of experts strategy that dynamically routes samples to experts exhibiting low uncertainty. This strategy effectively suppresses noise-induced instability, leading to enhanced robustness. Meanwhile, we design an uncertainty-guided routing to strengthen the multi-modal interaction, improving the performance. UGG-ReID is comprehensively evaluated on five representative multi-modal object ReID datasets, encompassing diverse spectral modalities. Experimental results show that the proposed method achieves excellent performance on all datasets and is significantly better than current methods in terms of noise immunity. Our code will be made public upon acceptance.
| null |
https://arxiv.org/abs/2507.04638v2
|
https://arxiv.org/pdf/2507.04638v2.pdf
| null |
[
"Xixi Wan",
"Aihua Zheng",
"Bo Jiang",
"Beibei Wang",
"Chenglong Li",
"Jin Tang"
] |
[
"Mixture-of-Experts"
] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/qs4d-quantization-aware-training-for
|
2507.06079
| null | null |
QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models
|
Structured State Space models (SSM) have recently emerged as a new class of deep learning models, particularly well-suited for processing long sequences. Their constant memory footprint, in contrast to the linearly scaling memory demands of Transformers, makes them attractive candidates for deployment on resource-constrained edge-computing devices. While recent works have explored the effect of quantization-aware training (QAT) on SSMs, they typically do not address its implications for specialized edge hardware, for example, analog in-memory computing (AIMC) chips. In this work, we demonstrate that QAT can significantly reduce the complexity of SSMs by up to two orders of magnitude across various performance metrics. We analyze the relation between model size and numerical precision, and show that QAT enhances robustness to analog noise and enables structural pruning. Finally, we integrate these techniques to deploy SSMs on a memristive analog in-memory computing substrate and highlight the resulting benefits in terms of computational efficiency.
| null |
https://arxiv.org/abs/2507.06079v1
|
https://arxiv.org/pdf/2507.06079v1.pdf
| null |
[
"Sebastian Siegel",
"Ming-Jay Yang",
"Younes Bouhadjar",
"Maxime Fabre",
"Emre Neftci",
"John Paul Strachan"
] |
[
"Computational Efficiency",
"Edge-computing",
"Quantization",
"State Space Models"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/optimal-placement-of-smart-hybrid
|
2507.05967
| null | null |
Optimal Placement of Smart Hybrid Transformers in Distribution Networks
|
Hybrid transformers are a relatively new technology that combine conventional power transformers with power electronics to provide voltage and reactive power control capabilities in distribution networks. This paper proposes a novel method of determining the optimal location and utilisation of hybrid transformers in 3-phase distribution networks to maximise the net present value of hybrid transformers based on their ability to increase the export of power produced by distributed generators over their operational lifespan. This has been accomplished through sequential linear programming, a key feature of which is the consideration of nonlinear characteristics and constraints relating to hybrid transformer power electronics and control capabilities. Test cases were carried out in a modified version of the Cigre European Low Voltage Distribution Network Benchmark, which has been extended by connecting it with two additional low voltage distribution test networks. All test case results demonstrate that the installation and utilisation of hybrid transformers can improve the income earned from exporting excess active power, justifying their installation cost (with the highest net present value being {\pounds}6.56 million, resulting from a 45.53 percent increase in estimated annual profits due to coordinated HT compensation).
| null |
https://arxiv.org/abs/2507.05967v1
|
https://arxiv.org/pdf/2507.05967v1.pdf
| null |
[
"Samuel Hayward",
"Martin Doff-Sotta",
"Michael Merlin",
"Matthew Williams",
"Thomas Morstyn"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-wireless-foundation-model-for-multi-task
|
2507.05938
| null | null |
A Wireless Foundation Model for Multi-Task Prediction
|
With the growing complexity and dynamics of the mobile communication networks, accurately predicting key system parameters, such as channel state information (CSI), user location, and network traffic, has become essential for a wide range of physical (PHY)-layer and medium access control (MAC)-layer tasks. Although traditional deep learning (DL)-based methods have been widely applied to such prediction tasks, they often struggle to generalize across different scenarios and tasks. In response, we propose a unified foundation model for multi-task prediction in wireless networks that supports diverse prediction intervals. The proposed model enforces univariate decomposition to unify heterogeneous tasks, encodes granularity for interval awareness, and uses a causal Transformer backbone for accurate predictions. Additionally, we introduce a patch masking strategy during training to support arbitrary input lengths. After trained on large-scale datasets, the proposed foundation model demonstrates strong generalization to unseen scenarios and achieves zero-shot performance on new tasks that surpass traditional full-shot baselines.
| null |
https://arxiv.org/abs/2507.05938v2
|
https://arxiv.org/pdf/2507.05938v2.pdf
| null |
[
"Yucheng Sheng",
"Jiacheng Wang",
"Xingyu Zhou",
"Le Liang",
"Hao Ye",
"Shi Jin",
"Geoffrey Ye Li"
] |
[
"model",
"Prediction",
"Prediction Intervals"
] | 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": "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/exploring-gain-doped-waveguide-synapse-for
|
2507.05931
| null | null |
Exploring Gain-Doped-Waveguide-Synapse for Neuromorphic Applications: A Pulsed Pump-Signal Approach
|
Neuromorphic computing promises to transform AI systems by enabling them to perceive, respond to, and adapt swiftly and accurately to dynamic data and user interactions. However, traditional silicon-based and hybrid electronic technologies for artificial neurons constrain neuromorphic processors in terms of flexibility, scalability, and energy efficiency. In this study, we pioneer the use of Doped-Gain-Layer-on-Waveguide-Synapses for bio-inspired neurons, utilizing a pulsed pump-signal mechanism to enhance neuromorphic computation. This approach addresses critical challenges in scalability and energy efficiency inherent in current technologies. We introduce the concept of Gain on Waveguide Dynamics for synapses, demonstrating how non-linear pulse transformations of input probe signals occur under various pump-probe configurations. Our findings reveal that primarily properties of pulse amplitude, period as well material properties such as doping densities and population dynamics influence strongly the generation of spiking responses that emulate neuronal behaviour and effectively how computational logic is. By harnessing the complex interactions of asynchronous spiking pump techniques and ion densities in excited states, our method produces event-driven responses that mirror natural neuronal functions. This gain-enhanced environment supports short-term memory capabilities alongside essential characteristics like asynchronous spike generation, threshold operation, and temporal integration, foundational to brain-inspired spiking neural network paradigms.
| null |
https://arxiv.org/abs/2507.05931v1
|
https://arxiv.org/pdf/2507.05931v1.pdf
| null |
[
"Robert Otupiri",
"Ripalta Stabile"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/universal-embeddings-of-tabular-data
|
2507.05904
| null | null |
Universal Embeddings of Tabular Data
|
Tabular data in relational databases represents a significant portion of industrial data. Hence, analyzing and interpreting tabular data is of utmost importance. Application tasks on tabular data are manifold and are often not specified when setting up an industrial database. To address this, we present a novel framework for generating universal, i.e., task-independent embeddings of tabular data for performing downstream tasks without predefined targets. Our method transforms tabular data into a graph structure, leverages Graph Auto-Encoders to create entity embeddings, which are subsequently aggregated to obtain embeddings for each table row, i.e., each data sample. This two-step approach has the advantage that unseen samples, consisting of similar entities, can be embedded without additional training. Downstream tasks such as regression, classification or outlier detection, can then be performed by applying a distance-based similarity measure in the embedding space. Experiments on real-world datasets demonstrate that our method achieves superior performance compared to existing universal tabular data embedding techniques.
| null |
https://arxiv.org/abs/2507.05904v1
|
https://arxiv.org/pdf/2507.05904v1.pdf
| null |
[
"Astrid Franz",
"Frederik Hoppe",
"Marianne Michaelis",
"Udo Göbel"
] |
[
"Entity Embeddings",
"Outlier Detection"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ai-reporter-a-path-to-a-new-genre-of
|
2507.05903
| null | null |
AI-Reporter: A Path to a New Genre of Scientific Communication
|
The AI-Reporter represents a paradigmatic shift in scientific publication practice. This document demonstrates through a concrete case study how our system transforms academic presentations into publication-ready chapters -- in less than three minutes. Using Arno Simons' lecture on Large Language Models from the ``Large Language Models for the History, Philosophy, and Sociology of Science'' workshop (NEPI) as an example, we show how technological innovation bridges the gap between ephemeral presentation and permanent scientific documentation.
| null |
https://arxiv.org/abs/2507.05903v1
|
https://arxiv.org/pdf/2507.05903v1.pdf
| null |
[
"Gerd Graßhoff"
] |
[
"Philosophy",
"Sociology"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/hierarchy-or-heterarchy-a-theory-of-long
|
2507.05888
| null | null |
Hierarchy or Heterarchy? A Theory of Long-Range Connections for the Sensorimotor Brain
|
In the traditional understanding of the neocortex, sensory information flows up a hierarchy of regions, with each level processing increasingly complex features. Information also flows down the hierarchy via a different set of connections. Although the hierarchical model has significant support, many anatomical connections do not conform to the standard hierarchical interpretation. In addition, hierarchically arranged regions sometimes respond in parallel, not sequentially as would occur in a hierarchy. This and other evidence suggests that two regions can act in parallel and hierarchically at the same time. Given this flexibility, the word "heterarchy" might be a more suitable term to describe neocortical organization. This paper proposes a new interpretation of how sensory and motor information is processed in the neocortex. The key to our proposal is what we call the "Thousand Brains Theory", which posits that every cortical column is a sensorimotor learning system. Columns learn by integrating sensory input over multiple movements of a sensor. In this view, even primary and secondary regions, such as V1 and V2, can learn and recognize complete 3D objects. This suggests that the hierarchical connections between regions are used to learn the compositional structure of parent objects composed of smaller child objects. We explain the theory by examining the different types of long-range connections between cortical regions and between the neocortex and thalamus. We describe these connections, and then suggest the specific roles they play in the context of a heterarchy of sensorimotor regions. We also suggest that the thalamus plays an essential role in transforming the pose between objects and sensors. The novel perspective we argue for here has broad implications for both neuroscience and artificial intelligence.
| null |
https://arxiv.org/abs/2507.05888v1
|
https://arxiv.org/pdf/2507.05888v1.pdf
| null |
[
"Jeff Hawkins",
"Niels Leadholm",
"Viviane Clay"
] |
[] | 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/ai-based-demand-forecasting-and-load
|
2507.06077
| null | null |
AI-Based Demand Forecasting and Load Balancing for Optimising Energy use in Healthcare Systems: A real case study
|
This paper tackles the urgent need for efficient energy management in healthcare facilities, where fluctuating demands challenge operational efficiency and sustainability. Traditional methods often prove inadequate, causing inefficiencies and higher costs. To address this, the study presents an AI-based framework combining Long Short-Term Memory (LSTM), genetic algorithm (GA), and SHAP (Shapley Additive Explanations), specifically designed for healthcare energy management. Although LSTM is widely used for time-series forecasting, its application in healthcare energy prediction remains underexplored. The results reveal that LSTM significantly outperforms ARIMA and Prophet models in forecasting complex, non-linear demand patterns. LSTM achieves a Mean Absolute Error (MAE) of 21.69 and Root Mean Square Error (RMSE) of 29.96, far better than Prophet (MAE: 59.78, RMSE: 81.22) and ARIMA (MAE: 87.73, RMSE: 125.22), demonstrating superior performance. The genetic algorithm is applied to optimize model parameters and improve load balancing strategies, enabling adaptive responses to real-time energy fluctuations. SHAP analysis further enhances model transparency by explaining the influence of different features on predictions, fostering trust in decision-making processes. This integrated LSTM-GA-SHAP approach offers a robust solution for improving forecasting accuracy, boosting energy efficiency, and advancing sustainability in healthcare facilities. Future research may explore real-time deployment and hybridization with reinforcement learning for continuous optimization. Overall, the study establishes a solid foundation for using AI in healthcare energy management, highlighting its scalability, efficiency, and resilience potential.
| null |
https://arxiv.org/abs/2507.06077v1
|
https://arxiv.org/pdf/2507.06077v1.pdf
| null |
[
"Iman Rahimi",
"Isha Patel"
] |
[
"Demand Forecasting",
"energy management",
"Management",
"Time Series Forecasting"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/fevo-financial-knowledge-expansion-and
|
2507.06057
| null | null |
FEVO: Financial Knowledge Expansion and Reasoning Evolution for Large Language Models
|
Advancements in reasoning for large language models (LLMs) have lead to significant performance improvements for LLMs in various fields such as mathematics and programming. However, research applying these advances to the financial domain, where considerable domain-specific knowledge is necessary to complete tasks, remains limited. To address this gap, we introduce FEVO (Financial Evolution), a multi-stage enhancement framework developed to enhance LLM performance in the financial domain. FEVO systemically enhances LLM performance by using continued pre-training (CPT) to expand financial domain knowledge, supervised fine-tuning (SFT) to instill structured, elaborate reasoning patterns, and reinforcement learning (RL) to further integrate the expanded financial domain knowledge with the learned structured reasoning. To ensure effective and efficient training, we leverage frontier reasoning models and rule-based filtering to curate FEVO-Train, high-quality datasets specifically designed for the different post-training phases. Using our framework, we train the FEVO series of models - C32B, S32B, R32B - from Qwen2.5-32B and evaluate them on seven benchmarks to assess financial and general capabilities, with results showing that FEVO-R32B achieves state-of-the-art performance on five financial benchmarks against much larger models as well as specialist models. More significantly, FEVO-R32B demonstrates markedly better performance than FEVO-R32B-0 (trained from Qwen2.5-32B-Instruct using only RL), thus validating the effectiveness of financial domain knowledge expansion and structured, logical reasoning distillation
| null |
https://arxiv.org/abs/2507.06057v2
|
https://arxiv.org/pdf/2507.06057v2.pdf
| null |
[
"Bo Pang",
"Yalu Ouyang",
"Hangfei Xu",
"Ziqi Jia",
"Panpan Li",
"Shengzhao Wen",
"Lu Wang",
"Shiyong Li",
"Yanpeng Wang"
] |
[
"Logical Reasoning",
"Reinforcement Learning (RL)"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/bluelm-2-5-3b-technical-report
|
2507.05934
| null | null |
BlueLM-2.5-3B Technical Report
|
We present BlueLM-2.5-3B, a compact and unified dense Multimodal Large Language Model (MLLM) designed for efficient edge-device deployment, offering strong general-purpose and reasoning capabilities. To the best of our knowledge, this is the first 3B-scale MLLM to support both thinking and non-thinking modes, while also enabling explicit control over thinking token budget. BlueLM-2.5-3B is developed through diversified data curation, key data resampling, hybrid heterogeneous reinforcement learning, and a high-performance training infrastructure. Our model achieves superior multimodal capacity while preserving competitive pure-text performance with only 2.9 billion parameters. We conduct comprehensive evaluations across a broad range of multimodal and text-only benchmarks. In thinking mode, BlueLM-2.5-3B achieves comparable performance to Qwen3-4B on text-only benchmarks, and trails the larger Kimi-VL-A3B-16B by only about 5% on average across multimodal evaluations. In non-thinking mode, it outperforms Qwen2.5-VL-3B on the majority of multimodal benchmarks. Additionally, BlueLM-2.5-3B exhibits exceptional data efficiency. All of the aforementioned performance is achieved with substantially less total training data than Qwen2.5-VL-3B and Qwen3-4B. We hope our work contributes to the advancement of high-performance, on-device MLLMs and provides meaningful insights to the research community.
| null |
https://arxiv.org/abs/2507.05934v1
|
https://arxiv.org/pdf/2507.05934v1.pdf
| null |
[
"Baojiao Xiong",
"Boheng Chen",
"Chengzhi Wang",
"Daxiong Luo",
"Dongsheng Xu",
"Dongyang Liu",
"Fan Yang",
"Fangyuan Li",
"Fei Teng",
"Feng Wang",
"Fukang Qin",
"Fuquan Peng",
"Guanxin Tan",
"Guozhi Wang",
"Haibo Yu",
"Haohao Gao",
"Heng Liu",
"Hongbo Yang",
"Hongjian Zou",
"Houzheng Shen",
"Hu Meng",
"Huan Li",
"Hui Tan",
"Jiali Chen",
"Jianzhao Chen",
"Jinliang Zhu",
"Kai Wang",
"Lei Wu",
"Liangbing Liu",
"Liuyang Bian",
"Liyan He",
"Long Liu",
"Peiwen Li",
"Penggang Shi",
"Qi Ding",
"Rui Hu",
"Shuai Cao",
"Shuai Ren",
"Shuang Peng",
"Teng Xie",
"Weiji Chen",
"Weilin Xiang",
"Weixin Wu",
"Xi Yin",
"Xiaoxin Chen",
"Xu Chen",
"Yafei Wen",
"Yan Hu",
"Yanzhou Yang",
"Yina Xie",
"Yinghao Chen",
"Yixuan Liao",
"Yu Geng",
"Yuanjiang Ouyang",
"Yuanzhuo Yang",
"Yuehua He",
"Yushuai Peng",
"Zhaoxiong Wang",
"Zheng Wang",
"Zhibo Zhou",
"Ziyang Wu"
] |
[
"Large Language Model",
"Multimodal Large Language Model"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/differentiable-reward-optimization-for-llm
|
2507.05911
| null | null |
Differentiable Reward Optimization for LLM based TTS system
|
This paper proposes a novel Differentiable Reward Optimization (DiffRO) method aimed at enhancing the performance of neural codec language models based text-to-speech (TTS) systems. In contrast to conventional reinforcement learning from human feedback (RLHF) approaches applied to TTS, DiffRO directly compute the rewards based on neural codec tokens, rather than relying on synthesized audio. Furthermore, we employ the Gumbel-Softmax technique to render the reward function differentiable, thereby streamlining the RLHF training process. Additionally, we introduce a multi-task reward (MTR) model which can provide feedback from different perspectives and find that it can augment the system's capability to follow instructions effectively.Experimental results indicate that DiffRO significantly improves the pronunciation accuracy of the TTS system, achieving state-of-the-art (SOTA) WER results on the seed-tts-eval benchmark. Moreover, with the integration of the MTR model, we demonstrate the ability to control emotional and quality attributes in a zero-shot manner.
|
This paper proposes a novel Differentiable Reward Optimization (DiffRO) method aimed at enhancing the performance of neural codec language models based text-to-speech (TTS) systems.
|
https://arxiv.org/abs/2507.05911v1
|
https://arxiv.org/pdf/2507.05911v1.pdf
| null |
[
"Changfeng Gao",
"Zhihao Du",
"Shiliang Zhang"
] |
[
"text-to-speech",
"Text to Speech"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/stable-acoustic-relay-assignment-with-high
|
2507.05900
| null | null |
Stable Acoustic Relay Assignment with High Throughput via Lase Chaos-based Reinforcement Learning
|
This study addresses the problem of stable acoustic relay assignment in an underwater acoustic network. Unlike the objectives of most existing literature, two distinct objectives, namely classical stable arrangement and ambiguous stable arrangement, are considered. To achieve these stable arrangements, a laser chaos-based multi-processing learning (LC-ML) method is introduced to efficiently obtain high throughput and rapidly attain stability. In order to sufficiently explore the relay's decision-making, this method uses random numbers generated by laser chaos to learn the assignment of relays to multiple source nodes. This study finds that the laser chaos-based random number and multi-processing in the exchange process have a positive effect on higher throughput and strong adaptability with environmental changing over time. Meanwhile, ambiguous cognitions result in the stable configuration with less volatility compared to accurate ones. This provides a practical and useful method and can be the basis for relay selection in complex underwater environments.
| null |
https://arxiv.org/abs/2507.05900v1
|
https://arxiv.org/pdf/2507.05900v1.pdf
| null |
[
"Zengjing Chen",
"Lu Wang",
"Chengzhi Xing"
] |
[
"Decision Making"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/gta1-gui-test-time-scaling-agent
|
2507.05791
| null | null |
GTA1: GUI Test-time Scaling Agent
|
Graphical user interface (GUI) agents autonomously operate across platforms (e.g., Linux) to complete tasks by interacting with visual elements. Specifically, a user instruction is decomposed into a sequence of action proposals, each corresponding to an interaction with the GUI. After each action, the agent observes the updated GUI environment to plan the next step. However, two main challenges arise: i) resolving ambiguity in task planning (i.e., the action proposal sequence), where selecting an appropriate plan is non-trivial, as many valid ones may exist; ii) accurately grounding actions in complex and high-resolution interfaces, i.e., precisely interacting with visual targets. This paper investigates the two aforementioned challenges with our GUI Test-time Scaling Agent, namely GTA1. First, to select the most appropriate action proposal, we introduce a test-time scaling method. At each step, we sample multiple candidate action proposals and leverage a judge model to evaluate and select the most suitable one. It trades off computation for better decision quality by concurrent sampling, shortening task execution steps, and improving overall performance. Second, we propose a model that achieves improved accuracy when grounding the selected action proposal to its corresponding visual elements. Our key insight is that reinforcement learning (RL) facilitates visual grounding through inherent objective alignments, rewarding successful clicks on interface elements. Experimentally, our method establishes state-of-the-art performance across diverse benchmarks. For example, GTA1-7B achieves 50.1%, 92.4%, and 67.7% accuracies on Screenspot-Pro, Screenspot-V2, and OSWorld-G, respectively. When paired with a planner applying our test-time scaling strategy, it exhibits state-of-the-art agentic performance (e.g., 45.2% task success rate on OSWorld). We open-source our code and models here.
|
Specifically, a user instruction is decomposed into a sequence of action proposals, each corresponding to an interaction with the GUI.
|
https://arxiv.org/abs/2507.05791v3
|
https://arxiv.org/pdf/2507.05791v3.pdf
| null |
[
"Yan Yang",
"Dongxu Li",
"Yutong Dai",
"Yuhao Yang",
"Ziyang Luo",
"Zirui Zhao",
"Zhiyuan Hu",
"Junzhe Huang",
"Amrita Saha",
"Zeyuan Chen",
"ran Xu",
"Liyuan Pan",
"Caiming Xiong",
"Junnan Li"
] |
[
"Reinforcement Learning (RL)",
"Task Planning",
"Visual Grounding"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/robust-bandwidth-estimation-for-real-time
|
2507.05785
| null | null |
Robust Bandwidth Estimation for Real-Time Communication with Offline Reinforcement Learning
|
Accurate bandwidth estimation (BWE) is critical for real-time communication (RTC) systems. Traditional heuristic approaches offer limited adaptability under dynamic networks, while online reinforcement learning (RL) suffers from high exploration costs and potential service disruptions. Offline RL, which leverages high-quality data collected from real-world environments, offers a promising alternative. However, challenges such as out-of-distribution (OOD) actions, policy extraction from behaviorally diverse datasets, and reliable deployment in production systems remain unsolved. We propose RBWE, a robust bandwidth estimation framework based on offline RL that integrates Q-ensemble (an ensemble of Q-functions) with a Gaussian mixture policy to mitigate OOD risks and enhance policy learning. A fallback mechanism ensures deployment stability by switching to heuristic methods under high uncertainty. Experimental results show that RBWE reduces overestimation errors by 18% and improves the 10th percentile Quality of Experience (QoE) by 18.6%, demonstrating its practical effectiveness in real-world RTC applications.
| null |
https://arxiv.org/abs/2507.05785v1
|
https://arxiv.org/pdf/2507.05785v1.pdf
| null |
[
"Jian Kai",
"Tianwei Zhang",
"Zihan Ling",
"Yang Cao",
"Can Shen"
] |
[
"Offline RL",
"Reinforcement Learning (RL)"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/hierarchical-task-offloading-for-uav-assisted
|
2507.05722
| null | null |
Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning
|
With the emergence of compute-intensive and delay-sensitive applications in vehicular networks, unmanned aerial vehicles (UAVs) have emerged as a promising complement for vehicular edge computing due to the high mobility and flexible deployment. However, the existing UAV-assisted offloading strategies are insufficient in coordinating heterogeneous computing resources and adapting to dynamic network conditions. Hence, this paper proposes a dual-layer UAV-assisted edge computing architecture based on partial offloading, composed of the relay capability of high-altitude UAVs and the computing support of low-altitude UAVs. The proposed architecture enables efficient integration and coordination of heterogeneous resources. A joint optimization problem is formulated to minimize the system delay and energy consumption while ensuring the task completion rate. To solve the high-dimensional decision problem, we reformulate the problem as a Markov decision process and propose a hierarchical offloading scheme based on the soft actor-critic algorithm. The method decouples global and local decisions, where the global decisions integrate offloading ratios and trajectory planning into continuous actions, while the local scheduling is handled via designing a priority-based mechanism. Simulations are conducted and demonstrate that the proposed approach outperforms several baselines in task completion rate, system efficiency, and convergence speed, showing strong robustness and applicability in dynamic vehicular environments.
| null |
https://arxiv.org/abs/2507.05722v1
|
https://arxiv.org/pdf/2507.05722v1.pdf
| null |
[
"Hongbao Li",
"Ziye Jia",
"Sijie He",
"Kun Guo",
"Qihui Wu"
] |
[
"Deep Reinforcement Learning",
"Edge-computing",
"Scheduling",
"Trajectory Planning"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/mobilegui-rl-advancing-mobile-gui-agent
|
2507.05720
| null | null |
MobileGUI-RL: Advancing Mobile GUI Agent through Reinforcement Learning in Online Environment
|
Recently, there has been a surge of vision-based GUI agents designed to automate everyday mobile and web tasks. These agents interpret raw GUI screenshots and autonomously decide where to click, scroll, or type, which bypasses handcrafted rules and app-specific APIs. However, most existing methods trained GUI agent in the offline environment using pre-collected trajectories. This approach limits scalability, causes overfitting to specific UI templates, and leads to brittle policies when faced with unseen environment. We present MobileGUI-RL, a scalable framework that trains GUI agent in online environment. MobileGUI-RL contains two key components. It (i) synthesizes a curriculum of learnable tasks through self-exploration and filtering, and (ii) adapts GRPO to GUI navigation with trajectory-aware advantages and composite rewards that balance task success and execution efficiency. Experiments on three online mobile-agent benchmarks show consistent gains, validating the effectiveness of our approach.
| null |
https://arxiv.org/abs/2507.05720v1
|
https://arxiv.org/pdf/2507.05720v1.pdf
| null |
[
"Yucheng Shi",
"Wenhao Yu",
"Zaitang Li",
"Yonglin Wang",
"Hongming Zhang",
"Ninghao Liu",
"Haitao Mi",
"Dong Yu"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/autotriton-automatic-triton-programming-with
|
2507.05687
| null | null |
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
|
Kernel development in deep learning requires optimizing computational units across hardware while balancing memory management, parallelism, and hardware-specific optimizations through extensive empirical tuning. Although domain-specific languages like Triton simplify GPU programming by abstracting low-level details, developers must still manually tune critical parameters such as tile sizes and memory access patterns through iterative experimentation, creating substantial barriers to optimal performance and wider adoption. In this work, we introduce AutoTriton, the first model dedicated to Triton programming powered by reinforcement learning (RL). AutoTriton performs supervised fine-tuning (SFT) to be equipped with essential Triton programming expertise using a high-quality data gathering pipeline, and conducts RL with Group Relative Policy Optimization (GRPO) algorithm, combining a rule-based reward and an execution-based reward to further improve Triton programming ability, sequentially. Experiments across five evaluation channels of TritonBench and KernelBench illustrate that our 8B model AutoTriton achieves performance comparable to mainstream large models, including Claude-4-Sonnet and DeepSeek-R1-0528. Further experimental analysis demonstrates the crucial role of each module within AutoTriton, including the SFT stage, the RL stage, and the reward design strategy. These findings underscore the promise of RL for automatically generating high-performance kernels, and since high-performance kernels are core components of AI systems, this breakthrough establishes an important foundation for building more efficient AI systems. The model and code will be available at https://github.com/AI9Stars/AutoTriton.
|
Kernel development in deep learning requires optimizing computational units across hardware while balancing memory management, parallelism, and hardware-specific optimizations through extensive empirical tuning.
|
https://arxiv.org/abs/2507.05687v1
|
https://arxiv.org/pdf/2507.05687v1.pdf
| null |
[
"Shangzhan Li",
"Zefan Wang",
"Ye He",
"YuXuan Li",
"Qi Shi",
"Jianling Li",
"Yonggang Hu",
"Wanxiang Che",
"Xu Han",
"Zhiyuan Liu",
"Maosong Sun"
] |
[
"GPU",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 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/tuneshield-mitigating-toxicity-in
|
2507.05660
| null | null |
TuneShield: Mitigating Toxicity in Conversational AI while Fine-tuning on Untrusted Data
|
Recent advances in foundation models, such as LLMs, have revolutionized conversational AI. Chatbots are increasingly being developed by customizing LLMs on specific conversational datasets. However, mitigating toxicity during this customization, especially when dealing with untrusted training data, remains a significant challenge. To address this, we introduce TuneShield, a defense framework designed to mitigate toxicity during chatbot fine-tuning while preserving conversational quality. TuneShield leverages LLM-based toxicity classification, utilizing the instruction-following capabilities and safety alignment of LLMs to effectively identify toxic samples, outperforming industry API services. TuneShield generates synthetic conversation samples, termed 'healing data', based on the identified toxic samples, using them to mitigate toxicity while reinforcing desirable behavior during fine-tuning. It performs an alignment process to further nudge the chatbot towards producing desired responses. Our findings show that TuneShield effectively mitigates toxicity injection attacks while preserving conversational quality, even when the toxicity classifiers are imperfect or biased. TuneShield proves to be resilient against adaptive adversarial and jailbreak attacks. Additionally, TuneShield demonstrates effectiveness in mitigating adaptive toxicity injection attacks during dialog-based learning (DBL).
| null |
https://arxiv.org/abs/2507.05660v1
|
https://arxiv.org/pdf/2507.05660v1.pdf
| null |
[
"Aravind Cheruvu",
"Shravya Kanchi",
"Sifat Muhammad Abdullah",
"Nicholas Kong",
"Daphne Yao",
"Murtuza Jadliwala",
"Bimal Viswanath"
] |
[
"Chatbot",
"Instruction Following",
"Safety Alignment"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/model-free-optical-processors-using-in-situ
|
2507.05583
| null | null |
Model-free Optical Processors using In Situ Reinforcement Learning with Proximal Policy Optimization
|
Optical computing holds promise for high-speed, energy-efficient information processing, with diffractive optical networks emerging as a flexible platform for implementing task-specific transformations. A challenge, however, is the effective optimization and alignment of the diffractive layers, which is hindered by the difficulty of accurately modeling physical systems with their inherent hardware imperfections, noise, and misalignments. While existing in situ optimization methods offer the advantage of direct training on the physical system without explicit system modeling, they are often limited by slow convergence and unstable performance due to inefficient use of limited measurement data. Here, we introduce a model-free reinforcement learning approach utilizing Proximal Policy Optimization (PPO) for the in situ training of diffractive optical processors. PPO efficiently reuses in situ measurement data and constrains policy updates to ensure more stable and faster convergence. We experimentally validated our method across a range of in situ learning tasks, including targeted energy focusing through a random diffuser, holographic image generation, aberration correction, and optical image classification, demonstrating in each task better convergence and performance. Our strategy operates directly on the physical system and naturally accounts for unknown real-world imperfections, eliminating the need for prior system knowledge or modeling. By enabling faster and more accurate training under realistic experimental constraints, this in situ reinforcement learning approach could offer a scalable framework for various optical and physical systems governed by complex, feedback-driven dynamics.
| null |
https://arxiv.org/abs/2507.05583v1
|
https://arxiv.org/pdf/2507.05583v1.pdf
| null |
[
"Yuhang Li",
"Shiqi Chen",
"Tingyu Gong",
"Aydogan Ozcan"
] |
[
"image-classification",
"Image Classification",
"Image Generation"
] | 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"
}
] |
https://paperswithcode.com/paper/epistemically-guided-forward-backward
|
2507.05477
| null | null |
Epistemically-guided forward-backward exploration
|
Zero-shot reinforcement learning is necessary for extracting optimal policies in absence of concrete rewards for fast adaptation to future problem settings. Forward-backward representations (FB) have emerged as a promising method for learning optimal policies in absence of rewards via a factorization of the policy occupancy measure. However, up until now, FB and many similar zero-shot reinforcement learning algorithms have been decoupled from the exploration problem, generally relying on other exploration algorithms for data collection. We argue that FB representations should fundamentally be used for exploration in order to learn more efficiently. With this goal in mind, we design exploration policies that arise naturally from the FB representation that minimize the posterior variance of the FB representation, hence minimizing its epistemic uncertainty. We empirically demonstrate that such principled exploration strategies improve sample complexity of the FB algorithm considerably in comparison to other exploration methods. Code is publicly available at https://sites.google.com/view/fbee-url.
| null |
https://arxiv.org/abs/2507.05477v1
|
https://arxiv.org/pdf/2507.05477v1.pdf
| null |
[
"Núria Armengol Urpí",
"Marin Vlastelica",
"Georg Martius",
"Stelian Coros"
] |
[
"reinforcement-learning",
"Reinforcement Learning"
] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/2048-reinforcement-learning-in-a-delayed
|
2507.05465
| null | null |
2048: Reinforcement Learning in a Delayed Reward Environment
|
Delayed and sparse rewards present a fundamental obstacle for reinforcement-learning (RL) agents, which struggle to assign credit for actions whose benefits emerge many steps later. The sliding-tile game 2048 epitomizes this challenge: although frequent small score changes yield immediate feedback, they often mislead agents into locally optimal but globally suboptimal strategies. In this work, we introduce a unified, distributional multi-step RL framework designed to directly optimize long-horizon performance. Using the open source Gym-2048 environment we develop and compare four agent variants: standard DQN, PPO, QR-DQN (Quantile Regression DQN), and a novel Horizon-DQN (H-DQN) that integrates distributional learning, dueling architectures, noisy networks, prioritized replay, and more. Empirical evaluation reveals a clear hierarchy in effectiveness: max episode scores improve from 3.988K (DQN) to 5.756K (PPO), 8.66K (QR-DQN), and 18.21K (H-DQN), with H-DQN reaching the 2048 tile. Upon scaling H-DQN it reaches a max score 41.828K and a 4096 tile. These results demonstrate that distributional, multi-step targets substantially enhance performance in sparse-reward domains, and they suggest promising avenues for further gains through model-based planning and curriculum learning.
| null |
https://arxiv.org/abs/2507.05465v1
|
https://arxiv.org/pdf/2507.05465v1.pdf
| null |
[
"Prady Saligram",
"Tanvir Bhathal",
"Robby Manihani"
] |
[
"quantile regression",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2025-07-07T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Q-Learning** is an off-policy temporal difference control algorithm:\r\n\r\n$$Q\\left(S\\_{t}, A\\_{t}\\right) \\leftarrow Q\\left(S\\_{t}, A\\_{t}\\right) + \\alpha\\left[R_{t+1} + \\gamma\\max\\_{a}Q\\left(S\\_{t+1}, a\\right) - Q\\left(S\\_{t}, A\\_{t}\\right)\\right] $$\r\n\r\nThe learned action-value function $Q$ directly approximates $q\\_{*}$, the optimal action-value function, independent of the policy being followed.\r\n\r\nSource: Sutton and Barto, Reinforcement Learning, 2nd Edition",
"full_name": "Q-Learning",
"introduced_year": 1984,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Off-Policy TD Control",
"parent": null
},
"name": "Q-Learning",
"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": "**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": "A **DQN**, or Deep Q-Network, approximates a state-value function in a [Q-Learning](https://paperswithcode.com/method/q-learning) framework with a neural network. In the Atari Games case, they take in several frames of the game as an input and output state values for each action as an output. \r\n\r\nIt is usually used in conjunction with [Experience Replay](https://paperswithcode.com/method/experience-replay), for storing the episode steps in memory for off-policy learning, where samples are drawn from the replay memory at random. Additionally, the Q-Network is usually optimized towards a frozen target network that is periodically updated with the latest weights every $k$ steps (where $k$ is a hyperparameter). The latter makes training more stable by preventing short-term oscillations from a moving target. The former tackles autocorrelation that would occur from on-line learning, and having a replay memory makes the problem more like a supervised learning problem.\r\n\r\nImage Source: [here](https://www.researchgate.net/publication/319643003_Autonomous_Quadrotor_Landing_using_Deep_Reinforcement_Learning)",
"full_name": "Deep Q-Network",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Q-Learning Networks",
"parent": "Off-Policy TD Control"
},
"name": "DQN",
"source_title": "Playing Atari with Deep Reinforcement Learning",
"source_url": "http://arxiv.org/abs/1312.5602v1"
},
{
"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"
}
] |
https://paperswithcode.com/paper/learn-globally-speak-locally-bridging-the
|
2507.05418
| null | null |
Learn Globally, Speak Locally: Bridging the Gaps in Multilingual Reasoning
|
Large Language Models (LLMs) have achieved strong performance in domains like mathematics, factual QA, and code generation, yet their multilingual reasoning capabilities in these tasks remain underdeveloped. Especially for low-resource languages such as Swahili or Thai, LLMs can often misinterpret prompts or default to reasoning in English. This implicit bias toward high-resource languages undermines factual accuracy, interpretability, and trust. Current multilingual benchmarks focus only on final answers, overlooking whether models actually reason in the target language. To address this gap, we introduce GeoFact-X, a geography-based multilingual factual reasoning benchmark with annotated reasoning traces in five languages: English, Hindi, Japanese, Swahili, and Thai. We further propose BRIDGE, a novel training method that guides supervised fine-tuning and test-time reinforcement learning with a language-consistency reward to align reasoning with the input language. Finally, we develop an automatic evaluation protocol using LLM-as-a-judge to assess answer correctness and the quality and language consistency of reasoning traces, enabling nuanced and scalable analysis beyond surface-level metrics. Our results show that BRIDGE significantly enhances multilingual reasoning fidelity, demonstrating that reasoning-aware multilingual reinforcement learning is crucial for robust cross-lingual generalization. https://jd730.github.io/projects/GeoFact-X_BRIDGE
| null |
https://arxiv.org/abs/2507.05418v1
|
https://arxiv.org/pdf/2507.05418v1.pdf
| null |
[
"Jaedong Hwang",
"Kumar Tanmay",
"Seok-Jin Lee",
"Ayush Agrawal",
"Hamid Palangi",
"Kumar Ayush",
"Ila Fiete",
"Paul Pu Liang"
] |
[
"Code Generation",
"reinforcement-learning",
"Reinforcement Learning"
] | 2025-07-07T00:00:00 | 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": "",
"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/reinforcement-fine-tuning-naturally-mitigates
|
2507.05386
| null | null |
Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training
|
Continual post-training (CPT) is a popular and effective technique for adapting foundation models like multimodal large language models to specific and ever-evolving downstream tasks. While existing research has primarily concentrated on methods like data replay, model expansion, or parameter regularization, the fundamental role of the learning paradigm within CPT remains largely unexplored. This paper presents a comparative analysis of two core post-training paradigms: supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT), investigating their respective impacts on knowledge retention during CPT. Our experiments are conducted on a benchmark comprising seven diverse multimodal tasks, utilizing Qwen2.5-VL-7B-Instruct as the base model for continual post-training. The investigation yields two significant findings: (1) When continuously learning on downstream tasks, SFT leads to catastrophic forgetting of previously learned tasks. In contrast, RFT inherently preserves prior knowledge and achieve performance comparable to multi-task training. (2) RFT successfully protects and even enhances the model's general knowledge on standard benchmarks (e.g., MMMU and MMLU-Pro). Conversely, SFT degrades general model capabilities severely. Further analysis shows that explicit mechanisms, such as KL penalty and chain-of-thought reasoning, are not the primary factors. Instead, we find that the implicit regularization inherent to RFT is a key factor in mitigating forgetting. Finally, we propose a rollout-based instance filtering algorithm to improve the stability and efficiency of RFT. Our comprehensive study demonstrates the superiority of RFT as a robust paradigm for continual post-training.
| null |
https://arxiv.org/abs/2507.05386v1
|
https://arxiv.org/pdf/2507.05386v1.pdf
| null |
[
"Song Lai",
"Haohan Zhao",
"Rong Feng",
"Changyi Ma",
"Wenzhuo LIU",
"Hongbo Zhao",
"Xi Lin",
"Dong Yi",
"Min Xie",
"Qingfu Zhang",
"Hongbin Liu",
"Gaofeng Meng",
"Fei Zhu"
] |
[
"General Knowledge",
"MMLU"
] | 2025-07-07T00:00:00 | 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"
},
{
"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/evortran-a-modern-fortran-package-for-genetic
|
2507.06082
| null | null |
evortran: a modern Fortran package for genetic algorithms with applications from LHC data fitting to LISA signal reconstruction
|
evortran is a modern Fortran library designed for high-performance genetic algorithms and evolutionary optimization. evortran can be used to tackle a wide range of problems in high-energy physics and beyond, such as derivative-free parameter optimization, complex search taks, parameter scans and fitting experimental data under the presence of instrumental noise. The library is built as an fpm package with flexibility and efficiency in mind, while also offering a simple installation process, user interface and integration into existing Fortran programs. evortran offers a variety of selection, crossover, mutation and elitism strategies, with which users can tailor an evolutionary algorithm to their specific needs. evortran supports different abstraction levels: from operating directly on individuals and populations, to running full evolutionary cycles, and even enabling migration between independently evolving populations to enhance convergence and maintain diversity. In this paper, we present the functionality of the evortran library, demonstrate its capabilities with example benchmark applications, and compare its performance with existing genetic algorithm frameworks. As physics-motivated applications, we use evortran to confront extended Higgs sectors with LHC data and to reconstruct gravitational wave spectra and the underlying physical parameters from LISA mock data, demonstrating its effectiveness in realistic, data-driven scenarios.
| null |
https://arxiv.org/abs/2507.06082v1
|
https://arxiv.org/pdf/2507.06082v1.pdf
| null |
[
"Thomas Biekötter"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/practical-design-and-performance-of-physical
|
2507.06063
| null | null |
Practical design and performance of physical reservoir computing using hysteresis
|
Physical reservoir computing is an innovative idea for using physical phenomena as computational resources. Recent research has revealed that information processing techniques can improve the performance, but for practical applications, it is equally important to study the level of performance with a simple design that is easy to construct experimentally. We focus on a reservoir composed of independent hysteretic systems as a model suitable for the practical implementation of physical reservoir computing. In this paper, we discuss the appropriate design of this reservoir, its performance, and its limitations. This research will serve as a practical guideline for constructing hysteresis-based reservoirs.
| null |
https://arxiv.org/abs/2507.06063v1
|
https://arxiv.org/pdf/2507.06063v1.pdf
| null |
[
"Yuhei Yamada"
] |
[] | 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/multi-view-mid-fusion-a-universal-approach
|
2507.06026
| null | null |
Multi-view mid fusion: a universal approach for learning in an HDLSS setting
|
The high-dimensional low-sample-size (HDLSS) setting presents significant challenges in various applications where the feature dimension far exceeds the number of available samples. This paper introduces a universal approach for learning in HDLSS setting using multi-view mid fusion techniques. It shows how existing mid fusion multi-view methods perform well in an HDLSS setting even if no inherent views are provided. Three view construction methods are proposed that split the high-dimensional feature vectors into smaller subsets, each representing a different view. Extensive experimental validation across model-types and learning tasks confirm the effectiveness and generalization of the approach. We believe the work in this paper lays the foundation for further research into the universal benefits of multi-view mid fusion learning.
| null |
https://arxiv.org/abs/2507.06026v1
|
https://arxiv.org/pdf/2507.06026v1.pdf
| null |
[
"Lynn Houthuys"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/conditional-multi-stage-failure-recovery-for
|
2507.06016
| null | null |
Conditional Multi-Stage Failure Recovery for Embodied Agents
|
Embodied agents performing complex tasks are susceptible to execution failures, motivating the need for effective failure recovery mechanisms. In this work, we introduce a conditional multistage failure recovery framework that employs zero-shot chain prompting. The framework is structured into four error-handling stages, with three operating during task execution and one functioning as a post-execution reflection phase. Our approach utilises the reasoning capabilities of LLMs to analyse execution challenges within their environmental context and devise strategic solutions. We evaluate our method on the TfD benchmark of the TEACH dataset and achieve state-of-the-art performance, outperforming a baseline without error recovery by 11.5% and surpassing the strongest existing model by 19%.
| null |
https://arxiv.org/abs/2507.06016v1
|
https://arxiv.org/pdf/2507.06016v1.pdf
| null |
[
"Youmna Farag",
"Svetlana Stoyanchev",
"Mohan Li",
"Simon Keizer",
"Rama Doddipatla"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/medical-world-model-generative-simulation-of
|
2506.02327
| null | null |
Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning
|
Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in simulating disease dynamics for clinical decision-making, leveraging recent advances in large generative models. To this end, we introduce the Medical World Model (MeWM), the first world model in medicine that visually predicts future disease states based on clinical decisions. MeWM comprises (i) vision-language models to serve as policy models, and (ii) tumor generative models as dynamics models. The policy model generates action plans, such as clinical treatments, while the dynamics model simulates tumor progression or regression under given treatment conditions. Building on this, we propose the inverse dynamics model that applies survival analysis to the simulated post-treatment tumor, enabling the evaluation of treatment efficacy and the selection of the optimal clinical action plan. As a result, the proposed MeWM simulates disease dynamics by synthesizing post-treatment tumors, with state-of-the-art specificity in Turing tests evaluated by radiologists. Simultaneously, its inverse dynamics model outperforms medical-specialized GPTs in optimizing individualized treatment protocols across all metrics. Notably, MeWM improves clinical decision-making for interventional physicians, boosting F1-score in selecting the optimal TACE protocol by 13%, paving the way for future integration of medical world models as the second readers.
| null |
https://arxiv.org/abs/2506.02327v1
|
https://arxiv.org/pdf/2506.02327v1.pdf
| null |
[
"Yijun Yang",
"Zhao-Yang Wang",
"Qiuping Liu",
"Shuwen Sun",
"Kang Wang",
"Rama Chellappa",
"Zongwei Zhou",
"Alan Yuille",
"Lei Zhu",
"Yu-Dong Zhang",
"Jieneng Chen"
] |
[
"Decision Making",
"Specificity",
"Survival Analysis"
] | 2025-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/where-what-why-towards-explainable-driver
|
2506.23088
| null | null |
Where, What, Why: Towards Explainable Driver Attention Prediction
|
Modeling task-driven attention in driving is a fundamental challenge for both autonomous vehicles and cognitive science. Existing methods primarily predict where drivers look by generating spatial heatmaps, but fail to capture the cognitive motivations behind attention allocation in specific contexts, which limits deeper understanding of attention mechanisms. To bridge this gap, we introduce Explainable Driver Attention Prediction, a novel task paradigm that jointly predicts spatial attention regions (where), parses attended semantics (what), and provides cognitive reasoning for attention allocation (why). To support this, we present W3DA, the first large-scale explainable driver attention dataset. It enriches existing benchmarks with detailed semantic and causal annotations across diverse driving scenarios, including normal conditions, safety-critical situations, and traffic accidents. We further propose LLada, a Large Language model-driven framework for driver attention prediction, which unifies pixel modeling, semantic parsing, and cognitive reasoning within an end-to-end architecture. Extensive experiments demonstrate the effectiveness of LLada, exhibiting robust generalization across datasets and driving conditions. This work serves as a key step toward a deeper understanding of driver attention mechanisms, with significant implications for autonomous driving, intelligent driver training, and human-computer interaction.
|
To bridge this gap, we introduce Explainable Driver Attention Prediction, a novel task paradigm that jointly predicts spatial attention regions (where), parses attended semantics (what), and provides cognitive reasoning for attention allocation (why).
|
https://arxiv.org/abs/2506.23088v1
|
https://arxiv.org/pdf/2506.23088v1.pdf
| null |
[
"Yuchen Zhou",
"Jiayu Tang",
"Xiaoyan Xiao",
"Yueyao Lin",
"Linkai Liu",
"Zipeng Guo",
"Hao Fei",
"Xiaobo Xia",
"Chao Gou"
] |
[
"Autonomous Driving",
"Autonomous Vehicles",
"Driver Attention Monitoring",
"Large Language Model",
"Prediction",
"Semantic Parsing"
] | 2025-06-29T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/eamamba-efficient-all-around-vision-state
|
2506.22246
| null | null |
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
|
Image restoration is a key task in low-level computer vision that aims to reconstruct high-quality images from degraded inputs. The emergence of Vision Mamba, which draws inspiration from the advanced state space model Mamba, marks a significant advancement in this field. Vision Mamba demonstrates excellence in modeling long-range dependencies with linear complexity, a crucial advantage for image restoration tasks. Despite its strengths, Vision Mamba encounters challenges in low-level vision tasks, including computational complexity that scales with the number of scanning sequences and local pixel forgetting. To address these limitations, this study introduces Efficient All-Around Mamba (EAMamba), an enhanced framework that incorporates a Multi-Head Selective Scan Module (MHSSM) with an all-around scanning mechanism. MHSSM efficiently aggregates multiple scanning sequences, which avoids increases in computational complexity and parameter count. The all-around scanning strategy implements multiple patterns to capture holistic information and resolves the local pixel forgetting issue. Our experimental evaluations validate these innovations across several restoration tasks, including super resolution, denoising, deblurring, and dehazing. The results validate that EAMamba achieves a significant 31-89% reduction in FLOPs while maintaining favorable performance compared to existing low-level Vision Mamba methods.
|
Despite its strengths, Vision Mamba encounters challenges in low-level vision tasks, including computational complexity that scales with the number of scanning sequences and local pixel forgetting.
|
https://arxiv.org/abs/2506.22246v1
|
https://arxiv.org/pdf/2506.22246v1.pdf
| null |
[
"Yu-Cheng Lin",
"Yu-Syuan Xu",
"Hao-Wei Chen",
"Hsien-Kai Kuo",
"Chun-Yi Lee"
] |
[
"All",
"Deblurring",
"Denoising",
"Image Restoration",
"Mamba",
"Super-Resolution"
] | 2025-06-27T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/state-spaces/mamba",
"description": "Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers’ computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5× higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pre-training and downstream evaluation.",
"full_name": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"introduced_year": 2000,
"main_collection": null,
"name": "Mamba",
"source_title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"source_url": "https://arxiv.org/abs/2312.00752v2"
}
] |
https://paperswithcode.com/paper/dreamrenderer-taming-multi-instance-attribute
|
2503.12885
| null | null |
DreamRenderer: Taming Multi-Instance Attribute Control in Large-Scale Text-to-Image Models
|
Image-conditioned generation methods, such as depth- and canny-conditioned approaches, have demonstrated remarkable abilities for precise image synthesis. However, existing models still struggle to accurately control the content of multiple instances (or regions). Even state-of-the-art models like FLUX and 3DIS face challenges, such as attribute leakage between instances, which limits user control. To address these issues, we introduce DreamRenderer, a training-free approach built upon the FLUX model. DreamRenderer enables users to control the content of each instance via bounding boxes or masks, while ensuring overall visual harmony. We propose two key innovations: 1) Bridge Image Tokens for Hard Text Attribute Binding, which uses replicated image tokens as bridge tokens to ensure that T5 text embeddings, pre-trained solely on text data, bind the correct visual attributes for each instance during Joint Attention; 2) Hard Image Attribute Binding applied only to vital layers. Through our analysis of FLUX, we identify the critical layers responsible for instance attribute rendering and apply Hard Image Attribute Binding only in these layers, using soft binding in the others. This approach ensures precise control while preserving image quality. Evaluations on the COCO-POS and COCO-MIG benchmarks demonstrate that DreamRenderer improves the Image Success Ratio by 17.7% over FLUX and enhances the performance of layout-to-image models like GLIGEN and 3DIS by up to 26.8%. Project Page: https://limuloo.github.io/DreamRenderer/.
| null |
https://arxiv.org/abs/2503.12885v2
|
https://arxiv.org/pdf/2503.12885v2.pdf
| null |
[
"Dewei Zhou",
"MingWei Li",
"Zongxin Yang",
"Yi Yang"
] |
[
"Attribute",
"Image Generation",
"POS"
] | 2025-03-17T00:00:00 | null | null | null | null |
[
{
"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": "A Gated Linear Unit, or GLU computes:\r\n\r\n$$\r\n\\mathrm{GLU}(a, b) = a \\otimes \\sigma(b)\r\n$$\r\n\r\nIt is used in natural language processing architectures, for example the Gated CNN, because here $\\sigma(b)$ is the gate that control what information from $a$ is passed up to the following layer. Intuitively, for a language modeling task, the gating mechanism allows selection of words or features that are important for predicting the next word. The GLU also has non-linear capabilities, but has a linear path for the gradient so diminishes the vanishing gradient problem.",
"full_name": "Gated Linear Unit",
"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": "Gated Linear Unit",
"source_title": "Language Modeling with Gated Convolutional Networks",
"source_url": "http://arxiv.org/abs/1612.08083v3"
},
{
"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/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/DeadAt0m/adafactor-pytorch/blob/561e627239c29c0be11256171a795b49e0404098/adafactor.py#L7",
"description": "**Adafactor** is a stochastic optimization method based on [Adam](https://paperswithcode.com/method/adam) that reduces memory usage while retaining the empirical benefits of adaptivity. This is achieved through maintaining a factored representation of the squared gradient accumulator across training steps. Specifically, by tracking moving averages of the row and column sums of the squared gradients for matrix-valued variables, we are able to reconstruct a low-rank approximation of the exponentially smoothed accumulator at each training step that is optimal with respect to the generalized Kullback-Leibler divergence. For an $n \\times m$ matrix, this reduces the memory requirements from $O(n m)$ to $O(n + m)$. \r\n\r\nInstead of defining the optimization algorithm in terms of absolute step sizes {$\\alpha_t$}$\\_{t=1}^T$, the authors define the optimization algorithm in terms of relative step sizes {$\\rho_t$}$\\_{t=1}^T$, which get multiplied by the scale of the parameters. The scale of a parameter vector or matrix is defined as the root-mean-square of its components, lower-bounded by a small constant $\\epsilon_2$. The reason for this lower bound is to allow zero-initialized parameters to escape 0. \r\n\r\nProposed hyperparameters are: $\\epsilon\\_{1} = 10^{-30}$, $\\epsilon\\_{2} = 10^{-3}$, $d=1$, $p\\_{t} = \\min\\left(10^{-2}, \\frac{1}{\\sqrt{t}}\\right)$, $\\hat{\\beta}\\_{2\\_{t}} = 1 - t^{-0.8}$.",
"full_name": "Adafactor",
"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": "Adafactor",
"source_title": "Adafactor: Adaptive Learning Rates with Sublinear Memory Cost",
"source_url": "http://arxiv.org/abs/1804.04235v1"
},
{
"code_snippet_url": null,
"description": "**Inverse Square Root** is a learning rate schedule 1 / $\\sqrt{\\max\\left(n, k\\right)}$ where\r\n$n$ is the current training iteration and $k$ is the number of warm-up steps. This sets a constant learning rate for the first $k$ steps, then exponentially decays the learning rate until pre-training is over.",
"full_name": "Inverse Square Root Schedule",
"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": "Inverse Square Root Schedule",
"source_title": null,
"source_url": null
},
{
"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": "",
"description": "**T5**, or **Text-to-Text Transfer Transformer**, is a [Transformer](https://paperswithcode.com/method/transformer) based architecture that uses a text-to-text approach. Every task – including translation, question answering, and classification – is cast as feeding the model text as input and training it to generate some target text. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks. The changes compared to [BERT](https://paperswithcode.com/method/bert) include:\r\n\r\n- adding a *causal* decoder to the bidirectional architecture.\r\n- replacing the fill-in-the-blank cloze task with a mix of alternative pre-training tasks.",
"full_name": "T5",
"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": "T5",
"source_title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
"source_url": "https://arxiv.org/abs/1910.10683v4"
}
] |
https://paperswithcode.com/paper/small-language-models-are-the-future-of
|
2506.02153
| null | null |
Small Language Models are the Future of Agentic AI
|
Large language models (LLMs) are often praised for exhibiting near-human performance on a wide range of tasks and valued for their ability to hold a general conversation. The rise of agentic AI systems is, however, ushering in a mass of applications in which language models perform a small number of specialized tasks repetitively and with little variation. Here we lay out the position that small language models (SLMs) are sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems, and are therefore the future of agentic AI. Our argumentation is grounded in the current level of capabilities exhibited by SLMs, the common architectures of agentic systems, and the economy of LM deployment. We further argue that in situations where general-purpose conversational abilities are essential, heterogeneous agentic systems (i.e., agents invoking multiple different models) are the natural choice. We discuss the potential barriers for the adoption of SLMs in agentic systems and outline a general LLM-to-SLM agent conversion algorithm. Our position, formulated as a value statement, highlights the significance of the operational and economic impact even a partial shift from LLMs to SLMs is to have on the AI agent industry. We aim to stimulate the discussion on the effective use of AI resources and hope to advance the efforts to lower the costs of AI of the present day. Calling for both contributions to and critique of our position, we commit to publishing all such correspondence at https://research.nvidia.com/labs/lpr/slm-agents.
| null |
https://arxiv.org/abs/2506.02153v1
|
https://arxiv.org/pdf/2506.02153v1.pdf
| null |
[
"Peter Belcak",
"Greg Heinrich",
"Shizhe Diao",
"Yonggan Fu",
"Xin Dong",
"Saurav Muralidharan",
"Yingyan Celine Lin",
"Pavlo Molchanov"
] |
[
"AI Agent",
"Position"
] | 2025-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/prompt-mechanisms-in-medical-imaging-a
|
2507.01055
| null | null |
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey
|
Deep learning offers transformative potential in medical imaging, yet its clinical adoption is frequently hampered by challenges such as data scarcity, distribution shifts, and the need for robust task generalization. Prompt-based methodologies have emerged as a pivotal strategy to guide deep learning models, providing flexible, domain-specific adaptations that significantly enhance model performance and adaptability without extensive retraining. This systematic review critically examines the burgeoning landscape of prompt engineering in medical imaging. We dissect diverse prompt modalities, including textual instructions, visual prompts, and learnable embeddings, and analyze their integration for core tasks such as image generation, segmentation, and classification. Our synthesis reveals how these mechanisms improve task-specific outcomes by enhancing accuracy, robustness, and data efficiency and reducing reliance on manual feature engineering while fostering greater model interpretability by making the model's guidance explicit. Despite substantial advancements, we identify persistent challenges, particularly in prompt design optimization, data heterogeneity, and ensuring scalability for clinical deployment. Finally, this review outlines promising future trajectories, including advanced multimodal prompting and robust clinical integration, underscoring the critical role of prompt-driven AI in accelerating the revolution of diagnostics and personalized treatment planning in medicine.
| null |
https://arxiv.org/abs/2507.01055v1
|
https://arxiv.org/pdf/2507.01055v1.pdf
| null |
[
"Hao Yang",
"Xinlong Liang",
"Zhang Li",
"Yue Sun",
"Zheyu Hu",
"Xinghe Xie",
"Behdad Dashtbozorg",
"Jincheng Huang",
"Shiwei Zhu",
"Luyi Han",
"Jiong Zhang",
"Shanshan Wang",
"Ritse Mann",
"Qifeng Yu",
"Tao Tan"
] |
[
"Feature Engineering",
"Image Generation",
"Prompt Engineering",
"Survey"
] | 2025-06-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/motion-generation-a-survey-of-generative
|
2507.05419
| null | null |
Motion Generation: A Survey of Generative Approaches and Benchmarks
|
Motion generation, the task of synthesizing realistic motion sequences from various conditioning inputs, has become a central problem in computer vision, computer graphics, and robotics, with applications ranging from animation and virtual agents to human-robot interaction. As the field has rapidly progressed with the introduction of diverse modeling paradigms including GANs, autoencoders, autoregressive models, and diffusion-based techniques, each approach brings its own advantages and limitations. This growing diversity has created a need for a comprehensive and structured review that specifically examines recent developments from the perspective of the generative approach employed. In this survey, we provide an in-depth categorization of motion generation methods based on their underlying generative strategies. Our main focus is on papers published in top-tier venues since 2023, reflecting the most recent advancements in the field. In addition, we analyze architectural principles, conditioning mechanisms, and generation settings, and compile a detailed overview of the evaluation metrics and datasets used across the literature. Our objective is to enable clearer comparisons and identify open challenges, thereby offering a timely and foundational reference for researchers and practitioners navigating the rapidly evolving landscape of motion generation.
| null |
https://arxiv.org/abs/2507.05419v1
|
https://arxiv.org/pdf/2507.05419v1.pdf
| null |
[
"Aliasghar Khani",
"Arianna Rampini",
"Bruno Roy",
"Larasika Nadela",
"Noa Kaplan",
"Evan Atherton",
"Derek Cheung",
"Jacky Bibliowicz"
] |
[
"Motion Generation",
"Survey"
] | 2025-07-07T00:00:00 | 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/hyperspectral-anomaly-detection-methods-a
|
2507.05730
| null | null |
Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study
|
Hyperspectral images are high-dimensional datasets comprising hundreds of contiguous spectral bands, enabling detailed analysis of materials and surfaces. Hyperspectral anomaly detection (HAD) refers to the technique of identifying and locating anomalous targets in such data without prior information about a hyperspectral scene or target spectrum. This technology has seen rapid advancements in recent years, with applications in agriculture, defence, military surveillance, and environmental monitoring. Despite this significant progress, existing HAD methods continue to face challenges such as high computational complexity, sensitivity to noise, and limited generalisation across diverse datasets. This study presents a comprehensive comparison of various HAD techniques, categorising them into statistical models, representation-based methods, classical machine learning approaches, and deep learning models. We evaluated these methods across 17 benchmarking datasets using different performance metrics, such as ROC, AUC, and separability map to analyse detection accuracy, computational efficiency, their strengths, limitations, and directions for future research. Our findings highlight that deep learning models achieved the highest detection accuracy, while statistical models demonstrated exceptional speed across all datasets. This survey aims to provide valuable insights for researchers and practitioners working to advance the field of hyperspectral anomaly detection methods.
| null |
https://arxiv.org/abs/2507.05730v2
|
https://arxiv.org/pdf/2507.05730v2.pdf
| null |
[
"Aayushma Pant",
"Arbind Agrahari Baniya",
"Tsz-Kwan Lee",
"Sunil Aryal"
] |
[
"Anomaly Detection",
"Benchmarking",
"Computational Efficiency",
"Survey"
] | 2025-07-08T00:00:00 | 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/large-language-models-for-crash-detection-in
|
2507.02074
| null | null |
Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges
|
Crash detection from video feeds is a critical problem in intelligent transportation systems. Recent developments in large language models (LLMs) and vision-language models (VLMs) have transformed how we process, reason about, and summarize multimodal information. This paper surveys recent methods leveraging LLMs for crash detection from video data. We present a structured taxonomy of fusion strategies, summarize key datasets, analyze model architectures, compare performance benchmarks, and discuss ongoing challenges and opportunities. Our review provides a foundation for future research in this fast-growing intersection of video understanding and foundation models.
| null |
https://arxiv.org/abs/2507.02074v1
|
https://arxiv.org/pdf/2507.02074v1.pdf
| null |
[
"Sanjeda Akter",
"Ibne Farabi Shihab",
"Anuj Sharma"
] |
[
"Video Understanding"
] | 2025-07-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-survey-on-vision-language-action-models-for-1
|
2506.24044
| null | null |
A Survey on Vision-Language-Action Models for Autonomous Driving
|
The rapid progress of multimodal large language models (MLLM) has paved the way for Vision-Language-Action (VLA) paradigms, which integrate visual perception, natural language understanding, and control within a single policy. Researchers in autonomous driving are actively adapting these methods to the vehicle domain. Such models promise autonomous vehicles that can interpret high-level instructions, reason about complex traffic scenes, and make their own decisions. However, the literature remains fragmented and is rapidly expanding. This survey offers the first comprehensive overview of VLA for Autonomous Driving (VLA4AD). We (i) formalize the architectural building blocks shared across recent work, (ii) trace the evolution from early explainer to reasoning-centric VLA models, and (iii) compare over 20 representative models according to VLA's progress in the autonomous driving domain. We also consolidate existing datasets and benchmarks, highlighting protocols that jointly measure driving safety, accuracy, and explanation quality. Finally, we detail open challenges - robustness, real-time efficiency, and formal verification - and outline future directions of VLA4AD. This survey provides a concise yet complete reference for advancing interpretable socially aligned autonomous vehicles. Github repo is available at \href{https://github.com/JohnsonJiang1996/Awesome-VLA4AD}{SicongJiang/Awesome-VLA4AD}.
|
The rapid progress of multimodal large language models (MLLM) has paved the way for Vision-Language-Action (VLA) paradigms, which integrate visual perception, natural language understanding, and control within a single policy.
|
https://arxiv.org/abs/2506.24044v1
|
https://arxiv.org/pdf/2506.24044v1.pdf
| null |
[
"Sicong Jiang",
"Zilin Huang",
"Kangan Qian",
"Ziang Luo",
"Tianze Zhu",
"Yang Zhong",
"Yihong Tang",
"Menglin Kong",
"Yunlong Wang",
"Siwen Jiao",
"Hao Ye",
"Zihao Sheng",
"Xin Zhao",
"Tuopu Wen",
"Zheng Fu",
"Sikai Chen",
"Kun Jiang",
"Diange Yang",
"Seongjin Choi",
"Lijun Sun"
] |
[
"Autonomous Driving",
"Autonomous Vehicles",
"Natural Language Understanding",
"Survey",
"Vision-Language-Action"
] | 2025-06-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/towards-transparent-ai-a-survey-on
|
2506.21812
| null | null |
Towards Transparent AI: A Survey on Explainable Large Language Models
|
Large Language Models (LLMs) have played a pivotal role in advancing Artificial Intelligence (AI). However, despite their achievements, LLMs often struggle to explain their decision-making processes, making them a 'black box' and presenting a substantial challenge to explainability. This lack of transparency poses a significant obstacle to the adoption of LLMs in high-stakes domain applications, where interpretability is particularly essential. To overcome these limitations, researchers have developed various explainable artificial intelligence (XAI) methods that provide human-interpretable explanations for LLMs. However, a systematic understanding of these methods remains limited. To address this gap, this survey provides a comprehensive review of explainability techniques by categorizing XAI methods based on the underlying transformer architectures of LLMs: encoder-only, decoder-only, and encoder-decoder models. Then these techniques are examined in terms of their evaluation for assessing explainability, and the survey further explores how these explanations are leveraged in practical applications. Finally, it discusses available resources, ongoing research challenges, and future directions, aiming to guide continued efforts toward developing transparent and responsible LLMs.
| null |
https://arxiv.org/abs/2506.21812v1
|
https://arxiv.org/pdf/2506.21812v1.pdf
| null |
[
"Avash Palikhe",
"Zhenyu Yu",
"Zichong Wang",
"Wenbin Zhang"
] |
[
"Decoder",
"Explainable artificial intelligence",
"Explainable Artificial Intelligence (XAI)",
"Survey"
] | 2025-06-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/thinking-with-images-for-multimodal-reasoning
|
2506.23918
| null | null |
Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers
|
Recent progress in multimodal reasoning has been significantly advanced by textual Chain-of-Thought (CoT), a paradigm where models conduct reasoning within language. This text-centric approach, however, treats vision as a static, initial context, creating a fundamental "semantic gap" between rich perceptual data and discrete symbolic thought. Human cognition often transcends language, utilizing vision as a dynamic mental sketchpad. A similar evolution is now unfolding in AI, marking a fundamental paradigm shift from models that merely think about images to those that can truly think with images. This emerging paradigm is characterized by models leveraging visual information as intermediate steps in their thought process, transforming vision from a passive input into a dynamic, manipulable cognitive workspace. In this survey, we chart this evolution of intelligence along a trajectory of increasing cognitive autonomy, which unfolds across three key stages: from external tool exploration, through programmatic manipulation, to intrinsic imagination. To structure this rapidly evolving field, our survey makes four key contributions. (1) We establish the foundational principles of the think with image paradigm and its three-stage framework. (2) We provide a comprehensive review of the core methods that characterize each stage of this roadmap. (3) We analyze the critical landscape of evaluation benchmarks and transformative applications. (4) We identify significant challenges and outline promising future directions. By providing this structured overview, we aim to offer a clear roadmap for future research towards more powerful and human-aligned multimodal AI.
|
(1) We establish the foundational principles of the think with image paradigm and its three-stage framework.
|
https://arxiv.org/abs/2506.23918v3
|
https://arxiv.org/pdf/2506.23918v3.pdf
| null |
[
"Zhaochen Su",
"Peng Xia",
"Hangyu Guo",
"Zhenhua Liu",
"Yan Ma",
"Xiaoye Qu",
"Jiaqi Liu",
"Yanshu Li",
"Kaide Zeng",
"Zhengyuan Yang",
"Linjie Li",
"Yu Cheng",
"Heng Ji",
"Junxian He",
"Yi R. Fung"
] |
[
"Multimodal Reasoning"
] | 2025-06-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/advancements-in-weed-mapping-a-systematic
|
2507.01269
| null | null |
Advancements in Weed Mapping: A Systematic Review
|
Weed mapping plays a critical role in precision management by providing accurate and timely data on weed distribution, enabling targeted control and reduced herbicide use. This minimizes environmental impacts, supports sustainable land management, and improves outcomes across agricultural and natural environments. Recent advances in weed mapping leverage ground-vehicle Red Green Blue (RGB) cameras, satellite and drone-based remote sensing combined with sensors such as spectral, Near Infra-Red (NIR), and thermal cameras. The resulting data are processed using advanced techniques including big data analytics and machine learning, significantly improving the spatial and temporal resolution of weed maps and enabling site-specific management decisions. Despite a growing body of research in this domain, there is a lack of comprehensive literature reviews specifically focused on weed mapping. In particular, the absence of a structured analysis spanning the entire mapping pipeline, from data acquisition to processing techniques and mapping tools, limits progress in the field. This review addresses these gaps by systematically examining state-of-the-art methods in data acquisition (sensor and platform technologies), data processing (including annotation and modelling), and mapping techniques (such as spatiotemporal analysis and decision support tools). Following PRISMA guidelines, we critically evaluate and synthesize key findings from the literature to provide a holistic understanding of the weed mapping landscape. This review serves as a foundational reference to guide future research and support the development of efficient, scalable, and sustainable weed management systems.
| null |
https://arxiv.org/abs/2507.01269v1
|
https://arxiv.org/pdf/2507.01269v1.pdf
| null |
[
"Mohammad Jahanbakht",
"Alex Olsen",
"Ross Marchant",
"Emilie Fillols",
"Mostafa Rahimi Azghadi"
] |
[
"Management"
] | 2025-07-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/visual-hand-gesture-recognition-with-deep
|
2507.04465
| null | null |
Visual Hand Gesture Recognition with Deep Learning: A Comprehensive Review of Methods, Datasets, Challenges and Future Research Directions
|
The rapid evolution of deep learning (DL) models and the ever-increasing size of available datasets have raised the interest of the research community in the always important field of vision-based hand gesture recognition (VHGR), and delivered a wide range of applications, such as sign language understanding and human-computer interaction using cameras. Despite the large volume of research works in the field, a structured and complete survey on VHGR is still missing, leaving researchers to navigate through hundreds of papers in order to find the right combination of data, model, and approach for each task. The current survey aims to fill this gap by presenting a comprehensive overview of this aspect of computer vision. With a systematic research methodology that identifies the state-of-the-art works and a structured presentation of the various methods, datasets, and evaluation metrics, this review aims to constitute a useful guideline for researchers, helping them to choose the right strategy for delving into a certain VHGR task. Starting with the methodology used for study selection, literature retrieval, and the analytical framing, the survey identifies and organizes key VHGR approaches using a taxonomy-based format in various dimensions such as input modality and application domain. The core of the survey provides an in-depth analysis of state-of-the-art techniques across three primary VHGR tasks: static gesture recognition, isolated dynamic gestures and continuous gesture recognition. For each task, the architectural trends and learning strategies are listed. Additionally, the study reviews commonly used datasets - emphasizing on annotation schemes - and evaluates standard performance metrics. It concludes by identifying major challenges in VHGR, including both general computer vision issues and domain-specific obstacles, and outlines promising directions for future research.
| null |
https://arxiv.org/abs/2507.04465v1
|
https://arxiv.org/pdf/2507.04465v1.pdf
| null |
[
"Konstantinos Foteinos",
"Jorgen Cani",
"Manousos Linardakis",
"Panagiotis Radoglou-Grammatikis",
"Vasileios Argyriou",
"Panagiotis Sarigiannidis",
"Iraklis Varlamis",
"Georgios Th. Papadopoulos"
] |
[
"Gesture Recognition",
"Hand Gesture Recognition",
"Hand-Gesture Recognition",
"Navigate",
"Survey"
] | 2025-07-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/point-cloud-compression-and-objective-quality
|
2506.22902
| null | null |
Point Cloud Compression and Objective Quality Assessment: A Survey
|
The rapid growth of 3D point cloud data, driven by applications in autonomous driving, robotics, and immersive environments, has led to criticals demand for efficient compression and quality assessment techniques. Unlike traditional 2D media, point clouds present unique challenges due to their irregular structure, high data volume, and complex attributes. This paper provides a comprehensive survey of recent advances in point cloud compression (PCC) and point cloud quality assessment (PCQA), emphasizing their significance for real-time and perceptually relevant applications. We analyze a wide range of handcrafted and learning-based PCC algorithms, along with objective PCQA metrics. By benchmarking representative methods on emerging datasets, we offer detailed comparisons and practical insights into their strengths and limitations. Despite notable progress, challenges such as enhancing visual fidelity, reducing latency, and supporting multimodal data remain. This survey outlines future directions, including hybrid compression frameworks and advanced feature extraction strategies, to enable more efficient, immersive, and intelligent 3D applications.
| null |
https://arxiv.org/abs/2506.22902v1
|
https://arxiv.org/pdf/2506.22902v1.pdf
| null |
[
"Yiling Xu",
"Yujie Zhang",
"Shuting Xia",
"Kaifa Yang",
"He Huang",
"Ziyu Shan",
"Wenjie Huang",
"Qi Yang",
"Le Yang"
] |
[
"Autonomous Driving",
"Benchmarking",
"Point Cloud Quality Assessment",
"Survey"
] | 2025-06-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/out-of-distribution-detection-in-3d
|
2507.00570
| null | null |
Out-of-distribution detection in 3D applications: a review
|
The ability to detect objects that are not prevalent in the training set is a critical capability in many 3D applications, including autonomous driving. Machine learning methods for object recognition often assume that all object categories encountered during inference belong to a closed set of classes present in the training data. This assumption limits generalization to the real world, as objects not seen during training may be misclassified or entirely ignored. As part of reliable AI, OOD detection identifies inputs that deviate significantly from the training distribution. This paper provides a comprehensive overview of OOD detection within the broader scope of trustworthy and uncertain AI. We begin with key use cases across diverse domains, introduce benchmark datasets spanning multiple modalities, and discuss evaluation metrics. Next, we present a comparative analysis of OOD detection methodologies, exploring model structures, uncertainty indicators, and distributional distance taxonomies, alongside uncertainty calibration techniques. Finally, we highlight promising research directions, including adversarially robust OOD detection and failure identification, particularly relevant to 3D applications. The paper offers both theoretical and practical insights into OOD detection, showcasing emerging research opportunities such as 3D vision integration. These insights help new researchers navigate the field more effectively, contributing to the development of reliable, safe, and robust AI systems.
| null |
https://arxiv.org/abs/2507.00570v1
|
https://arxiv.org/pdf/2507.00570v1.pdf
| null |
[
"Zizhao Li",
"Xueyang Kang",
"Joseph West",
"Kourosh Khoshelham"
] |
[
"Autonomous Driving",
"Navigate",
"Object Recognition",
"Out-of-Distribution Detection"
] | 2025-07-01T00:00:00 | 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/3d-shape-generation-a-survey
|
2506.22678
| null | null |
3D Shape Generation: A Survey
|
Recent advances in deep learning have significantly transformed the field of 3D shape generation, enabling the synthesis of complex, diverse, and semantically meaningful 3D objects. This survey provides a comprehensive overview of the current state of the art in 3D shape generation, organizing the discussion around three core components: shape representations, generative modeling approaches, and evaluation protocols. We begin by categorizing 3D representations into explicit, implicit, and hybrid setups, highlighting their structural properties, advantages, and limitations. Next, we review a wide range of generation methods, focusing on feedforward architectures. We further summarize commonly used datasets and evaluation metrics that assess fidelity, diversity, and realism of generated shapes. Finally, we identify open challenges and outline future research directions that could drive progress in controllable, efficient, and high-quality 3D shape generation. This survey aims to serve as a valuable reference for researchers and practitioners seeking a structured and in-depth understanding of this rapidly evolving field.
| null |
https://arxiv.org/abs/2506.22678v1
|
https://arxiv.org/pdf/2506.22678v1.pdf
| null |
[
"Nicolas Caytuiro",
"Ivan Sipiran"
] |
[
"3D Shape Generation",
"Diversity",
"Survey"
] | 2025-06-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/advancing-talking-head-generation-a
|
2507.02900
| null | null |
Advancing Talking Head Generation: A Comprehensive Survey of Multi-Modal Methodologies, Datasets, Evaluation Metrics, and Loss Functions
|
Talking Head Generation (THG) has emerged as a transformative technology in computer vision, enabling the synthesis of realistic human faces synchronized with image, audio, text, or video inputs. This paper provides a comprehensive review of methodologies and frameworks for talking head generation, categorizing approaches into 2D--based, 3D--based, Neural Radiance Fields (NeRF)--based, diffusion--based, parameter-driven techniques and many other techniques. It evaluates algorithms, datasets, and evaluation metrics while highlighting advancements in perceptual realism and technical efficiency critical for applications such as digital avatars, video dubbing, ultra-low bitrate video conferencing, and online education. The study identifies challenges such as reliance on pre--trained models, extreme pose handling, multilingual synthesis, and temporal consistency. Future directions include modular architectures, multilingual datasets, hybrid models blending pre--trained and task-specific layers, and innovative loss functions. By synthesizing existing research and exploring emerging trends, this paper aims to provide actionable insights for researchers and practitioners in the field of talking head generation. For the complete survey, code, and curated resource list, visit our GitHub repository: https://github.com/VineetKumarRakesh/thg.
|
Talking Head Generation (THG) has emerged as a transformative technology in computer vision, enabling the synthesis of realistic human faces synchronized with image, audio, text, or video inputs.
|
https://arxiv.org/abs/2507.02900v1
|
https://arxiv.org/pdf/2507.02900v1.pdf
| null |
[
"Vineet Kumar Rakesh",
"Soumya Mazumdar",
"Research Pratim Maity",
"Sarbajit Pal",
"Amitabha Das",
"Tapas Samanta"
] |
[
"NeRF",
"Talking Head Generation"
] | 2025-06-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/crop-pest-classification-using-deep-learning
|
2507.01494
| null | null |
Crop Pest Classification Using Deep Learning Techniques: A Review
|
Insect pests continue to bring a serious threat to crop yields around the world, and traditional methods for monitoring them are often slow, manual, and difficult to scale. In recent years, deep learning has emerged as a powerful solution, with techniques like convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid models gaining popularity for automating pest detection. This review looks at 37 carefully selected studies published between 2018 and 2025, all focused on AI-based pest classification. The selected research is organized by crop type, pest species, model architecture, dataset usage, and key technical challenges. The early studies relied heavily on CNNs but latest work is shifting toward hybrid and transformer-based models that deliver higher accuracy and better contextual understanding. Still, challenges like imbalanced datasets, difficulty in detecting small pests, limited generalizability, and deployment on edge devices remain significant hurdles. Overall, this review offers a structured overview of the field, highlights useful datasets, and outlines the key challenges and future directions for AI-based pest monitoring systems.
| null |
https://arxiv.org/abs/2507.01494v1
|
https://arxiv.org/pdf/2507.01494v1.pdf
| null |
[
"Muhammad Hassam Ejaz",
"Muhammad Bilal",
"Usman Habib"
] |
[
"Deep Learning"
] | 2025-07-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/integrating-multi-modal-sensors-a-review-of
|
2506.21885
| null | null |
Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles
|
Multi-sensor fusion plays a critical role in enhancing perception for autonomous driving, overcoming individual sensor limitations, and enabling comprehensive environmental understanding. This paper first formalizes multi-sensor fusion strategies into data-level, feature-level, and decision-level categories and then provides a systematic review of deep learning-based methods corresponding to each strategy. We present key multi-modal datasets and discuss their applicability in addressing real-world challenges, particularly in adverse weather conditions and complex urban environments. Additionally, we explore emerging trends, including the integration of Vision-Language Models (VLMs), Large Language Models (LLMs), and the role of sensor fusion in end-to-end autonomous driving, highlighting its potential to enhance system adaptability and robustness. Our work offers valuable insights into current methods and future directions for multi-sensor fusion in autonomous driving.
| null |
https://arxiv.org/abs/2506.21885v1
|
https://arxiv.org/pdf/2506.21885v1.pdf
| null |
[
"Chuheng Wei",
"Ziye Qin",
"Ziyan Zhang",
"Guoyuan Wu",
"Matthew J. Barth"
] |
[
"Autonomous Driving",
"Sensor Fusion"
] | 2025-06-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/integrated-open-source-framework-for
|
2507.06337
| null | null |
Integrated Open-Source Framework for Simulation of Transcatheter Pulmonary Valves in Native Right Ventricular Outflow Tracts
|
Background - Pulmonary insufficiency is a consequence of transannular patch repair in Tetralogy of Fallot (ToF), leading to late morbidity and mortality. Transcatheter native outflow tract pulmonary valve replacement (TPVR) has become common, but assessment of patient candidacy and selection of the optimal device remains challenging. We demonstrate an integrated open-source workflow for simulation of TPVR in image-derived models to inform device selection. Methods - Machine learning-based segmentation of CT scans was implemented to define the right ventricular outflow tract (RVOT). A custom workflow for device positioning and pre-compression was implemented in SlicerHeart. Resulting geometries were exported to FEBio for simulation. Visualization of results and quantification were performed using custom metrics implemented in SlicerHeart and FEBio. Results - RVOT model creation and device placement could be completed in under 1 minute. Virtual device placement using FE simulations visually mimicked actual device placement and allowed quantification of vessel strain, stress, and contact area. Regions of higher strain and stress were observed at the proximal and distal end locations of the TPVs where the devices impinge the RVOT wall. No other consistent trends were observed across simulations. The observed variability in mechanical metrics across RVOTS, stents, and locations in the RVOT highlights that no single device performs optimally in all anatomies, thereby reinforcing the need for simulation-based patient-specific assessment. Conclusions - This study demonstrates the feasibility of a novel open-source workflow for the rapid simulation of TPVR which with further refinement may inform assessment of patient candidacy and optimal device selection.
| null |
https://arxiv.org/abs/2507.06337v1
|
https://arxiv.org/pdf/2507.06337v1.pdf
| null |
[
"Christopher N. Zelonis",
"Jalaj Maheshwari",
"Wensi Wu",
"Steve A. Maas",
"Seda Aslan",
"Kyle Sunderland",
"Stephen Ching",
"Ashley Koluda",
"Yuval Barak-Corren",
"Nicolas Mangine",
"Patricia M. Sabin",
"Andras Lasso",
"Devin W. Laurence",
"Christian Herz",
"Matthew J. Gillespie",
"Jeffrey A. Weiss",
"Matthew A. Jolley"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/effects-of-structure-on-reasoning-in-instance-1
|
2507.03347
| null | null |
Effects of structure on reasoning in instance-level Self-Discover
|
The drive for predictable LLM reasoning in their integration with compound systems has popularized structured outputs, yet concerns remain about performance trade-offs compared to unconstrained natural language. At the same time, training on unconstrained Chain of Thought (CoT) traces has brought about a new class of strong reasoning models that nevertheless present novel compute budget and faithfulness challenges. This paper introduces iSelf-Discover, an instance-level adaptation of the Self-Discover framework, and using it compares dynamically generated structured JSON reasoning with its unstructured counterpart. Our empirical evaluation across diverse benchmarks using state-of-the-art open-source models supports a consistent advantage for unstructured reasoning. Notably, on the complex MATH benchmark, unstructured plans achieved relative performance improvements of up to 18.90\% over structured approaches. Zero-shot unstructured iSelf-Discover variants are also shown to outperform their five-shot structured counterparts, underscoring the significance of this gap, even when structured plans are dynamically generated to ensure reasoning precedes the final answer. We further demonstrate that the optimal granularity of plan generation (instance-level vs. task-level) is context-dependent. These findings invite re-evaluation of the reliance on structured formats for complex problem-solving and how compound systems should be organized.
|
The drive for predictable LLM reasoning in their integration with compound systems has popularized structured outputs, yet concerns remain about performance trade-offs compared to unconstrained natural language.
|
https://arxiv.org/abs/2507.03347v1
|
https://arxiv.org/pdf/2507.03347v1.pdf
| null |
[
"Sachith Gunasekara",
"Yasiru Ratnayake"
] |
[
"Math"
] | 2025-07-04T00:00:00 |
https://arxiv.org/abs/2507.03347v1
|
https://arxiv.org/pdf/2507.03347v1
|
effects-of-structure-on-reasoning-in-instance
| null |
[] |
https://paperswithcode.com/paper/pacgdc-label-efficient-generalizable-depth
|
2507.07374
| null | null |
PacGDC: Label-Efficient Generalizable Depth Completion with Projection Ambiguity and Consistency
|
Generalizable depth completion enables the acquisition of dense metric depth maps for unseen environments, offering robust perception capabilities for various downstream tasks. However, training such models typically requires large-scale datasets with metric depth labels, which are often labor-intensive to collect. This paper presents PacGDC, a label-efficient technique that enhances data diversity with minimal annotation effort for generalizable depth completion. PacGDC builds on novel insights into inherent ambiguities and consistencies in object shapes and positions during 2D-to-3D projection, allowing the synthesis of numerous pseudo geometries for the same visual scene. This process greatly broadens available geometries by manipulating scene scales of the corresponding depth maps. To leverage this property, we propose a new data synthesis pipeline that uses multiple depth foundation models as scale manipulators. These models robustly provide pseudo depth labels with varied scene scales, affecting both local objects and global layouts, while ensuring projection consistency that supports generalization. To further diversify geometries, we incorporate interpolation and relocation strategies, as well as unlabeled images, extending the data coverage beyond the individual use of foundation models. Extensive experiments show that PacGDC achieves remarkable generalizability across multiple benchmarks, excelling in diverse scene semantics/scales and depth sparsity/patterns under both zero-shot and few-shot settings. Code: https://github.com/Wang-xjtu/PacGDC.
|
This paper presents PacGDC, a label-efficient technique that enhances data diversity with minimal annotation effort for generalizable depth completion.
|
https://arxiv.org/abs/2507.07374v1
|
https://arxiv.org/pdf/2507.07374v1.pdf
| null |
[
"Haotian Wang",
"Aoran Xiao",
"Xiaoqin Zhang",
"Meng Yang",
"Shijian Lu"
] |
[
"Depth Completion"
] | 2025-07-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/goal-oriented-sequential-bayesian
|
2507.07359
| null | null |
Goal-Oriented Sequential Bayesian Experimental Design for Causal Learning
|
We present GO-CBED, a goal-oriented Bayesian framework for sequential causal experimental design. Unlike conventional approaches that select interventions aimed at inferring the full causal model, GO-CBED directly maximizes the expected information gain (EIG) on user-specified causal quantities of interest, enabling more targeted and efficient experimentation. The framework is both non-myopic, optimizing over entire intervention sequences, and goal-oriented, targeting only model aspects relevant to the causal query. To address the intractability of exact EIG computation, we introduce a variational lower bound estimator, optimized jointly through a transformer-based policy network and normalizing flow-based variational posteriors. The resulting policy enables real-time decision-making via an amortized network. We demonstrate that GO-CBED consistently outperforms existing baselines across various causal reasoning and discovery tasks-including synthetic structural causal models and semi-synthetic gene regulatory networks-particularly in settings with limited experimental budgets and complex causal mechanisms. Our results highlight the benefits of aligning experimental design objectives with specific research goals and of forward-looking sequential planning.
| null |
https://arxiv.org/abs/2507.07359v1
|
https://arxiv.org/pdf/2507.07359v1.pdf
| null |
[
"Zheyu Zhang",
"Jiayuan Dong",
"Jie Liu",
"Xun Huan"
] |
[
"Experimental Design"
] | 2025-07-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/samo-a-lightweight-sharpness-aware-approach
|
2507.07883
| null | null |
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation
|
Multi-task learning (MTL) enables a joint model to capture commonalities across multiple tasks, reducing computation costs and improving data efficiency. However, a major challenge in MTL optimization is task conflicts, where the task gradients differ in direction or magnitude, limiting model performance compared to single-task counterparts. Sharpness-aware minimization (SAM) minimizes task loss while simultaneously reducing the sharpness of the loss landscape. Our empirical observations show that SAM effectively mitigates task conflicts in MTL. Motivated by these findings, we explore integrating SAM into MTL but face two key challenges. While both the average loss gradient and individual task gradients-referred to as global and local information-contribute to SAM, how to combine them remains unclear. Moreover, directly computing each task gradient introduces significant computational and memory overheads. To address these challenges, we propose SAMO, a lightweight \textbf{S}harpness-\textbf{A}ware \textbf{M}ulti-task \textbf{O}ptimization approach, that leverages a joint global-local perturbation. The local perturbations are approximated using only forward passes and are layerwise normalized to improve efficiency. Extensive experiments on a suite of multi-task benchmarks demonstrate both the effectiveness and efficiency of our method. Code is available at https://github.com/OptMN-Lab/SAMO.
|
Sharpness-aware minimization (SAM) minimizes task loss while simultaneously reducing the sharpness of the loss landscape.
|
https://arxiv.org/abs/2507.07883v1
|
https://arxiv.org/pdf/2507.07883v1.pdf
| null |
[
"Hao Ban",
"Gokul Ram Subramani",
"Kaiyi Ji"
] |
[
"Multi-Task Learning"
] | 2025-07-10T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Segment Anything Model",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Segmentation Models",
"parent": null
},
"name": "SAM",
"source_title": "Segment Anything",
"source_url": "https://arxiv.org/abs/2304.02643v1"
},
{
"code_snippet_url": null,
"description": "**Sharpness-Aware Minimization**, or **SAM**, is a procedure that improves model generalization by simultaneously minimizing loss value and loss sharpness. SAM functions by seeking parameters that lie in neighborhoods having uniformly low loss value (rather than parameters that only themselves have low loss value).",
"full_name": "Sharpness-Aware Minimization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Optimization",
"parent": null
},
"name": "Sharpness-Aware Minimization",
"source_title": "Sharpness-Aware Minimization for Efficiently Improving Generalization",
"source_url": "https://arxiv.org/abs/2010.01412v3"
}
] |
https://paperswithcode.com/paper/colors-see-colors-ignore-clothes-changing
| null | null | null |
Colors See Colors Ignore: Clothes Changing ReID with Color Disentanglement (ICCV-25 🥳)
|
Clothes-Changing Re-Identification (CC-ReID) aims to recognize individuals across different locations and times, irrespective of clothing. Existing methods often rely on additional models or annotations to learn robust, clothing-invariant features, making them resource-intensive. In contrast, we explore the use of color—specifically foreground and background colors—as a lightweight, annotation-free proxy for mitigating appearance bias in ReID models. We propose Colors See, Colors Ignore (CSCI), an RGB-only method that leverages color information directly from raw images or video frames. CSCI efficiently captures color-related appearance bias (‘Color See’) while disentangling it from identity-relevant ReID features (‘Color Ignore’). To achieve this, we introduce S2A self-attention, a novel self-attention to prevent information leak between color and identity cues within the feature space. Our analysis shows a strong correspondence between learned color embeddings and clothing attributes, validating color as an effective proxy when explicit clothing labels are unavailable. We demonstrate the effectiveness of CSCI on both image and video ReID with extensive experiments on four CC-ReID datasets. We improve the baseline by Top-1 2.9% on LTCC and 5.0% on PRCC for image-based ReID, and 1.0% on CCVID and 2.5% on MeVID for video-based ReID without relying on additional supervision. Our results highlight the potential of color as a cost-effective solution for addressing appearance bias in CC-ReID.
|
We improve the baseline by Top-1 2. 9% on LTCC and 5. 0% on PRCC for image-based ReID, and 1. 0% on CCVID and 2. 5% on MeVID for video-based ReID without relying on additional supervision.
|
https://arxiv.org/abs/2507.07230
|
https://arxiv.org/pdf/2507.07230
| null |
[
"Priyank Pathak",
"Yogesh S Rawat"
] |
[
"Disentanglement",
"Person Re-Identification"
] | 2025-06-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ost-bench-evaluating-the-capabilities-of
|
2507.07984
| null | null |
OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding
|
Recent advances in multimodal large language models (MLLMs) have shown remarkable capabilities in integrating vision and language for complex reasoning. While most existing benchmarks evaluate models under offline settings with a fixed set of pre-recorded inputs, we introduce OST-Bench, a benchmark designed to evaluate Online Spatio-Temporal understanding from the perspective of an agent actively exploring a scene. The Online aspect emphasizes the need to process and reason over incrementally acquired observations, while the Spatio-Temporal component requires integrating current visual inputs with historical memory to support dynamic spatial reasoning. OST-Bench better reflects the challenges of real-world embodied perception. Built on an efficient data collection pipeline, OST-Bench consists of 1.4k scenes and 10k question-answer pairs collected from ScanNet, Matterport3D, and ARKitScenes. We evaluate several leading MLLMs on OST-Bench and observe that they fall short on tasks requiring complex spatio-temporal reasoning. Under the online setting, their accuracy declines as the exploration horizon extends and the memory grows. Through further experimental analysis, we identify common error patterns across models and find that both complex clue-based spatial reasoning demands and long-term memory retrieval requirements significantly drop model performance along two separate axes, highlighting the core challenges that must be addressed to improve online embodied reasoning. To foster further research and development in the field, our codes, dataset, and benchmark are available. Our project page is: https://rbler1234.github.io/OSTBench.github.io/
|
Recent advances in multimodal large language models (MLLMs) have shown remarkable capabilities in integrating vision and language for complex reasoning.
|
https://arxiv.org/abs/2507.07984v1
|
https://arxiv.org/pdf/2507.07984v1.pdf
| null |
[
"Jingli Lin",
"Chenming Zhu",
"Runsen Xu",
"Xiaohan Mao",
"Xihui Liu",
"Tai Wang",
"Jiangmiao Pang"
] |
[
"Scene Understanding",
"Spatial Reasoning"
] | 2025-07-10T00:00:00 | 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/objectomaly-objectness-aware-refinement-for
|
2507.07460
| null | null |
Objectomaly: Objectness-Aware Refinement for OoD Segmentation with Structural Consistency and Boundary Precision
|
Out-of-Distribution (OoD) segmentation is critical for safety-sensitive applications like autonomous driving. However, existing mask-based methods often suffer from boundary imprecision, inconsistent anomaly scores within objects, and false positives from background noise. We propose \textbf{\textit{Objectomaly}}, an objectness-aware refinement framework that incorporates object-level priors. Objectomaly consists of three stages: (1) Coarse Anomaly Scoring (CAS) using an existing OoD backbone, (2) Objectness-Aware Score Calibration (OASC) leveraging SAM-generated instance masks for object-level score normalization, and (3) Meticulous Boundary Precision (MBP) applying Laplacian filtering and Gaussian smoothing for contour refinement. Objectomaly achieves state-of-the-art performance on key OoD segmentation benchmarks, including SMIYC AnomalyTrack/ObstacleTrack and RoadAnomaly, improving both pixel-level (AuPRC up to 96.99, FPR$_{95}$ down to 0.07) and component-level (F1$-$score up to 83.44) metrics. Ablation studies and qualitative results on real-world driving videos further validate the robustness and generalizability of our method. Code will be released upon publication.
|
Out-of-Distribution (OoD) segmentation is critical for safety-sensitive applications like autonomous driving.
|
https://arxiv.org/abs/2507.07460v1
|
https://arxiv.org/pdf/2507.07460v1.pdf
| null |
[
"Jeonghoon Song",
"Sunghun Kim",
"Jaegyun Im",
"Byeongjoon Noh"
] |
[
"Autonomous Driving"
] | 2025-07-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/rethinking-query-based-transformer-for-1
|
2507.07831
| null | null |
Rethinking Query-based Transformer for Continual Image Segmentation
|
Class-incremental/Continual image segmentation (CIS) aims to train an image segmenter in stages, where the set of available categories differs at each stage. To leverage the built-in objectness of query-based transformers, which mitigates catastrophic forgetting of mask proposals, current methods often decouple mask generation from the continual learning process. This study, however, identifies two key issues with decoupled frameworks: loss of plasticity and heavy reliance on input data order. To address these, we conduct an in-depth investigation of the built-in objectness and find that highly aggregated image features provide a shortcut for queries to generate masks through simple feature alignment. Based on this, we propose SimCIS, a simple yet powerful baseline for CIS. Its core idea is to directly select image features for query assignment, ensuring "perfect alignment" to preserve objectness, while simultaneously allowing queries to select new classes to promote plasticity. To further combat catastrophic forgetting of categories, we introduce cross-stage consistency in selection and an innovative "visual query"-based replay mechanism. Experiments demonstrate that SimCIS consistently outperforms state-of-the-art methods across various segmentation tasks, settings, splits, and input data orders. All models and codes will be made publicly available at https://github.com/SooLab/SimCIS.
|
To address these, we conduct an in-depth investigation of the built-in objectness and find that highly aggregated image features provide a shortcut for queries to generate masks through simple feature alignment.
|
https://arxiv.org/abs/2507.07831v1
|
https://arxiv.org/pdf/2507.07831v1.pdf
|
CVPR 2025 1
|
[
"Yuchen Zhu",
"Cheng Shi",
"Dingyou Wang",
"Jiajin Tang",
"Zhengxuan Wei",
"Yu Wu",
"Guanbin Li",
"Sibei Yang"
] |
[
"Continual Learning",
"Image Segmentation",
"Semantic Segmentation"
] | 2025-07-10T00:00:00 |
http://openaccess.thecvf.com//content/CVPR2025/html/Zhu_Rethinking_Query-based_Transformer_for_Continual_Image_Segmentation_CVPR_2025_paper.html
|
http://openaccess.thecvf.com//content/CVPR2025/papers/Zhu_Rethinking_Query-based_Transformer_for_Continual_Image_Segmentation_CVPR_2025_paper.pdf
|
rethinking-query-based-transformer-for
| 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/mvnet-hyperspectral-remote-sensing-image
|
2507.04409
| null | null |
MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture
|
Hyperspectral image (HSI) classification faces challenges such as high-dimensional data, limited training samples, and spectral redundancy, which often lead to overfitting and insufficient generalization capability. This paper proposes a novel MVNet network architecture that integrates 3D-CNN's local feature extraction, Transformer's global modeling, and Mamba's linear complexity sequence modeling capabilities, achieving efficient spatial-spectral feature extraction and fusion. MVNet features a redesigned dual-branch Mamba module, including a State Space Model (SSM) branch and a non-SSM branch employing 1D convolution with SiLU activation, enhancing modeling of both short-range and long-range dependencies while reducing computational latency in traditional Mamba. The optimized HSI-MambaVision Mixer module overcomes the unidirectional limitation of causal convolution, capturing bidirectional spatial-spectral dependencies in a single forward pass through decoupled attention that focuses on high-value features, alleviating parameter redundancy and the curse of dimensionality. On IN, UP, and KSC datasets, MVNet outperforms mainstream hyperspectral image classification methods in both classification accuracy and computational efficiency, demonstrating robust capability in processing complex HSI data.
|
Hyperspectral image (HSI) classification faces challenges such as high-dimensional data, limited training samples, and spectral redundancy, which often lead to overfitting and insufficient generalization capability.
|
https://arxiv.org/abs/2507.04409v1
|
https://arxiv.org/pdf/2507.04409v1.pdf
| null |
[
"Guandong Li",
"Mengxia Ye"
] |
[
"Computational Efficiency",
"Hyperspectral Image Classification",
"image-classification",
"Image Classification",
"Mamba",
"Remote Sensing Image Classification"
] | 2025-07-06T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/hendrycks/GELUs/blob/master/twitter_pos.py#L178",
"description": "** Sigmoid Linear Units**, or **SiLUs**, are activation functions for\r\nneural networks. The activation of the SiLU is computed by the sigmoid function multiplied by its input, or $$ x\\sigma(x).$$\r\n\r\nSee [Gaussian Error Linear Units](https://arxiv.org/abs/1606.08415) ([GELUs](https://paperswithcode.com/method/gelu)) where the SiLU was originally coined, and see [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://arxiv.org/abs/1702.03118) and [Swish: a Self-Gated Activation Function](https://arxiv.org/abs/1710.05941v1) where the SiLU was experimented with later.",
"full_name": "Sigmoid Linear Unit",
"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": "SiLU",
"source_title": "Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning",
"source_url": "http://arxiv.org/abs/1702.03118v3"
},
{
"code_snippet_url": "https://github.com/state-spaces/mamba",
"description": "Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers’ computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5× higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pre-training and downstream evaluation.",
"full_name": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"introduced_year": 2000,
"main_collection": null,
"name": "Mamba",
"source_title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"source_url": "https://arxiv.org/abs/2312.00752v2"
},
{
"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
}
] |
https://paperswithcode.com/paper/muvod-a-novel-multi-view-video-object
|
2507.07519
| null | null |
MUVOD: A Novel Multi-view Video Object Segmentation Dataset and A Benchmark for 3D Segmentation
|
The application of methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3D GS) have steadily gained popularity in the field of 3D object segmentation in static scenes. These approaches demonstrate efficacy in a range of 3D scene understanding and editing tasks. Nevertheless, the 4D object segmentation of dynamic scenes remains an underexplored field due to the absence of a sufficiently extensive and accurately labelled multi-view video dataset. In this paper, we present MUVOD, a new multi-view video dataset for training and evaluating object segmentation in reconstructed real-world scenarios. The 17 selected scenes, describing various indoor or outdoor activities, are collected from different sources of datasets originating from various types of camera rigs. Each scene contains a minimum of 9 views and a maximum of 46 views. We provide 7830 RGB images (30 frames per video) with their corresponding segmentation mask in 4D motion, meaning that any object of interest in the scene could be tracked across temporal frames of a given view or across different views belonging to the same camera rig. This dataset, which contains 459 instances of 73 categories, is intended as a basic benchmark for the evaluation of multi-view video segmentation methods. We also present an evaluation metric and a baseline segmentation approach to encourage and evaluate progress in this evolving field. Additionally, we propose a new benchmark for 3D object segmentation task with a subset of annotated multi-view images selected from our MUVOD dataset. This subset contains 50 objects of different conditions in different scenarios, providing a more comprehensive analysis of state-of-the-art 3D object segmentation methods. Our proposed MUVOD dataset is available at https://volumetric-repository.labs.b-com.com/#/muvod.
| null |
https://arxiv.org/abs/2507.07519v1
|
https://arxiv.org/pdf/2507.07519v1.pdf
| null |
[
"Bangning Wei",
"Joshua Maraval",
"Meriem Outtas",
"Kidiyo Kpalma",
"Nicolas Ramin",
"Lu Zhang"
] |
[
"NeRF",
"Object",
"Scene Understanding",
"Segmentation",
"Semantic Segmentation",
"Video Object Segmentation",
"Video Segmentation",
"Video Semantic Segmentation"
] | 2025-07-10T00:00:00 | null | null | null | null |
[] |
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