Improve model card: Add metadata tags, abstract, links, usage, and benchmarks (#1)

- Improve model card: Add metadata tags, abstract, links, usage, and benchmarks (10781e8b80e66cca3b5eee91733e5ae242bab516)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,12 +1,174 @@
 ---
-license: mit
+base_model:
+- Qwen/Qwen2.5-Omni-3B
 datasets:
 - openinterx/UGC-VideoCap
+license: mit
 metrics:
 - bleu
 - accuracy
-base_model:
-- Qwen/Qwen2.5-Omni-3B
+pipeline_tag: video-text-to-text
+library_name: transformers
+tags:
+  - multimodal
+  - video-captioning
+  - audio-visual
+  - ugc
 ---
 
-paper: https://arxiv.org/abs/2507.11336
+# UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New Benchmarks
+
+**UGC-VideoCaptioner** is a 3B-parameter captioning model distilled from Gemini-2.5 Flash, specifically designed for detailed, omnimodal captioning of short-form, user-generated videos (UGC). It addresses the crucial role of audio in conjunction with visual content, which is often overlooked in existing video captioning models.
+
+<p align="left">
+  <a href="https://huggingface.co/papers/2507.11336" target="_blank">
+    <img src="https://img.shields.io/badge/HuggingFace_Paper-2507.11336%F0%9F%93%96-blue">
+  </a>
+  <a href="http://arxiv.org/abs/2507.11336" target="_blank">
+    <img src="https://img.shields.io/badge/arXiv-2507.11336%F0%9F%93%96-bron">
+  </a>
+  <a href="https://memories.ai/" target="_blank">
+    <img src="https://img.shields.io/badge/Project_Page-%F0%9F%8C%90-green">
+  </a>
+  <a href="https://github.com/openinterx/UGC-VideoCaptioner" target="_blank">
+    <img src="https://img.shields.io/badge/GitHub-Code-black?logo=github">
+  </a>
+</p>
+
+## Abstract
+
+Real-world user-generated videos, especially on platforms like TikTok, often feature rich and intertwined audio visual content. However, existing video captioning benchmarks and models remain predominantly visual centric, overlooking the crucial role of audio in conveying scene dynamics, speaker intent, and narrative context. This lack of omni datasets and lightweight, capable models hampers progress in fine grained, multimodal video understanding. To address these challenges, we introduce UGC-VideoCap, a new benchmark and model framework specifically designed for detailed omnimodal captioning of short form user-generated videos. Unlike prior datasets, UGC-VideoCap emphasizes balanced integration of audio and visual modalities, featuring 1000 TikTok videos annotated through a structured three stage human-in-the-loop pipeline covering audio only, visual only, and joint audio visual semantics. The benchmark also includes 4000 carefully crafted QA pairs probing both unimodal and cross modal understanding. Alongside the dataset, we propose UGC-VideoCaptioner(3B), a 3B parameter captioning model distilled from Gemini 2.5 Flash. Using a novel two-stage training strategy supervised fine tuning followed by Group Relative Policy Optimization (GRPO), our approach enables efficient adaptation from limited data while maintaining competitive performance. Together, our benchmark and model offer a high-quality foundation and a data-efficient solution for advancing omnimodal video captioning in unconstrained real-world UGC settings.
+
+<p align="center">
+    <img src="https://github.com/openinterx/UGC-VideoCaptioner/blob/main/tiktok_qa_sample.png?raw=true" alt="UGC-VideoCap">
+</p>
+
+## Benchmark Results
+
+<p align="center">
+    <img src="https://github.com/openinterx/UGC-VideoCaptioner/blob/main/benchmark.png?raw=true" alt="UGC-VideoCap">
+</p>
+
+### Model Zoom
+
+| Model                         | visual | audio | details | average | Link |
+|:------------------------------|:------:|:-----:|:-------:|:-------:|:----:|
+| Gemini-2.5-pro              |    75.8    |   70.8    |    74.8     |     73.78    | N/A  |
+| Gemini-2.5-flash              | **78.8**     |   **74.2**    |    **77.2**    |    **76.73**    | N/A  |
+| Qwen2.5-Omni-3B               |   55.6     |  48.2    |   52.6      |   52.18      | N/A  |
+| UGC-VideoCaptioner-3B-zero(1k RL)         |    57.8    |  53.0     |    55.4     |    55.40(**+3.22**)    | [google-drive](https://drive.google.com/drive/folders/1R-L4kz4R7UxYpcU4El1ctgvVDQbsMsG6?usp=sharing) |
+| Qwen2.5-Omni-3B 1k sft        |    58.4    |   61.4  |   57.0    |     58.96(+6.78)    | [google-drive](https://drive.google.com/drive/folders/1itJ1u4XEJNVfmgbxuKL-fGWCbaW3EAza?usp=sharing) |
+| Qwen2.5-Omni-3B 10k sft       |    58.4   |    63.2   |   58.0     |   59.87(+7.69)     | [google-drive](https://drive.google.com/drive/folders/1auQ4mx9CcxIzAIF4SyH034xufzrqe29w?usp=sharing) |
+| Qwen2.5-Omni-3B 20k sft       |    59.2   |   64    |    58.4   |     60.50(+8.32)     | [google-drive](https://drive.google.com/drive/folders/11WJZkq8I_807zJUmBCCvwNjSj18F2im9?usp=sharing) |
+| UGC-VideoCaptioner-3B (1k SFT + 1k RL)         |   59.4     |    62.4   |    58.2     |    60.01(**+7.83**)   | [google-drive](https://drive.google.com/drive/folders/1LGmIU60cdacErNgUk86D8I5_kiU_ljFz?usp=sharing) |
+
+## Quick Start
+
+You can use this model with the `transformers` library. Below is a quick example demonstrating how to perform inference.
+Please note that for full video processing capabilities, you might need to install `decord` and refer to the [official GitHub repository](https://github.com/openinterx/UGC-VideoCaptioner) for detailed video handling steps, especially if `AutoProcessor` doesn't directly handle video file paths for complex scenarios.
+
+### Environment Setup
+
+```bash
+pip install transformers torch decord soundfile qwen_omni_utils
+```
+
+### Inference
+
+```python
+from transformers import AutoProcessor, AutoModelForConditionalGeneration
+import torch
+
+# Load model and processor
+model = AutoModelForConditionalGeneration.from_pretrained(
+    "openinterx/UGC-VideoCaptioner-3B",
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True, # Required for custom architectures/processors
+)
+processor = AutoProcessor.from_pretrained(
+    "openinterx/UGC-VideoCaptioner-3B",
+    trust_remote_code=True, # Required for custom architectures/processors
+)
+
+# Example video path (replace with your actual video file path)
+video_path = "path/to/your/video.mp4" 
+
+# Define the detailed captioning prompt
+prompt_text = (
+    "You are given a short video with both audio and visual content. Write a detailed and coherent paragraph "
+    "that naturally integrates all modalities. Your description should include: (1) the primary scene and "
+    "background setting; (2) key characters or objects and their actions or interactions; (3) significant "
+    "audio cues such as voices, background music, sound effects, and their emotional tone; (4) any on-screen "
+    "text (OCR) and its role in the video context; and (5) the overall theme or purpose of the video. "
+    "Ensure the output is a fluent and objective paragraph, not a bullet-point list, and captures the video's "
+    "content in a human-like, narrative style."
+)
+
+# Prepare messages in the chat template format
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "video", "video": video_path}, # Pass video path
+            {"type": "text", "text": prompt_text},
+        ],
+    }
+]
+
+# Apply chat template and process inputs
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+
+inputs = processor(
+    text=[text],
+    videos=[video_path], # Pass video path directly to the processor
+    return_tensors="pt",
+).to(model.device)
+
+# Generate output
+generated_ids = model.generate(**inputs, max_new_tokens=512)
+
+# Decode generated text
+output_text = processor.batch_decode(
+    generated_ids[:, inputs["input_ids"].shape[1]:], # Exclude prompt from output
+    skip_special_tokens=True,
+    clean_up_tokenization_spaces=False
+)[0]
+
+print(output_text)
+```
+
+## Evaluation
+
+### Final Caption prompt (for inference)
+
+```python
+prompt = "You are given a short video with both audio and visual content. Write a detailed and coherent paragraph that naturally integrates all modalities. "
+"Your description should include: (1) the primary scene and background setting; (2) key characters or objects and their actions or interactions; "
+"(3) significant audio cues such as voices, background music, sound effects, and their emotional tone; "
+"(4) any on-screen text (OCR) and its role in the video context; and (5) the overall theme or purpose of the video. "
+"Ensure the output is a fluent and objective paragraph, not a bullet-point list, and captures the video's content in a human-like, narrative style.
+```
+
+### Score
+
+Scores are judged by GPT-4o-2024-08-06.
+
+```bash
+python eval_caption.py
+```
+
+## Citation
+
+If you find this repository helpful, feel free to cite our paper:
+
+```bibtex
+@article{wu2024ugc,
+  title={UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New Benchmarks},
+  author={Wu, Zhenyu and Sun, Qiushi and Zhang, Jiabo and Zhu, Yuyin and Ma, Guojun and Cheng, Kanzhi and Jia, Chengyou and Tan, Jian and Yang, Qing and Wu, Zhiyong},
+  journal={arXiv preprint arXiv:2507.11336},
+  year={2024}
+}
+```