Improve dataset card: Add comprehensive content, links, and sample usage

README.md

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-license: mit
-task_categories:
-- video-text-to-text
 language:
 - en
+license: mit
 size_categories:
 - 1K<n<10K
+task_categories:
+- video-text-to-text
 ---
-<!-- <div align="center">
-  <h1>RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video</h1> 
-</div> -->
 
-<!-- [![hf_checkpoint](https://img.shields.io/badge/🤗-RTV--Bench-9C276A.svg)](https://huggingface.co/datasets/xunsh/RTV-Bench)  -->
+# $\mathcal{RTV}\text{-}Bench$: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video
+
+[![arXiv](https://img.shields.io/badge/arXiv-2505.02064-b31b1b.svg?style=plastic)](https://arxiv.org/abs/2505.02064) [![hf_checkpoint](https://img.shields.io/badge/🤗-RTV--Bench-9C276A.svg)](https://huggingface.co/datasets/xunsh/RTV-Bench) [![ms_checkpoint](https://img.shields.io/badge/🤖-RTV--Bench-8A2BE2.svg)](https://www.modelscope.cn/datasets/Jungang/RTV-Bench)  
+[Paper](https://huggingface.co/papers/2505.02064) | [Project Page](https://ljungang.github.io/RTV-Bench) | [Code](https://github.com/ljungang/rtv-bench)
+
+## 🔥 News
+*   **`2025-09-20`** 🎉🎉🎉 Our paper has been accepted by NeurIPS 2025, we will update our dataset and code for community as soon as possible~
+*   **`2025-06-27`** 🎉 We update core code for evaluation.
+*   **`2025-05-17`** 🎉 We have released the label json, which is named `QA.json`.
+*   **`2025-05-04`** 🎉  We released the paper $\mathcal{RTV}\text{-}Bench$: [Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video](https://arxiv.org/abs/2505.02064).
+*   **`2025-05-03`** 🌟 We are happy to release the $\mathcal{RTV}\text{-}Bench$. You can find the $\mathcal{RTV}\text{-}Bench$ from [![hf_checkpoint](https://img.shields.io/badge/🤗-RTV--Bench-9C276A.svg)](https://huggingface.co/datasets/xunsh/RTV-Bench) or [![ms_checkpoint](https://img.shields.io/badge/🤖-RTV--Bench-8A2BE2.svg)](https://www.modelscope.cn/datasets/Jungang/RTV-Bench).
+<p align="center">
+    <img src="https://github.com/ljungang/rtv-bench/blob/main/asset/1_examples.png?raw=true" width="100%" height="100%" >
+</p>
 
-<!-- [![ms_checkpoint](https://img.shields.io/badge/🤖-RTV--Bench-8A2BE2.svg)](https://www.modelscope.cn/datasets/Jungang/RTV-Bench)   -->
-<!-- ## 🔥 News
-* **`2025.05.03`** 🌟 We are happy to release the RTV-Bench.
-  
 ## TODO
-- [ ] Release the final label json.
-- [ ] Release the evaluation code.
+- [x] Release the final label json.
+- [x] Release the evaluation code.
 - [ ] Construct a more comprehensive benchmark for real-time video analysis.
 - [ ] ···
-## 👀 RTV-Bench Overview
 
-We introduce RTV-Bench, a fine-grained benchmark for MLLM real-time video analysis, which contains **552** videos (167.2 hours) and **4,631** high-quality QA pairs. We evaluated leading MLLMs, including proprietary (GPT-4o, Gemini 2.0), open-source offline (Qwen2.5-VL, VideoLLaMA3), and open-source real-time (VITA-1.5, InternLM-XComposer2.5-OmniLive) models. Experiment results show open-source real-time models largely outperform offline ones but still trail top proprietary models. Our analysis also reveals that larger model size or higher frame sampling rates do not significantly boost $\mathcal{RTV}\text{-}Bench$ performance, sometimes causing slight decreases. This underscores the need for better model architectures optimized for video stream processing and long sequences to advance real-time video analysis with MLLMs.  $\mathcal{RTV}\text{-}Bench$ includes three key principles: 
-* **Multi-Timestamp Question Answering (MTQA)**, where answers evolve with scene changes; 
-* **Hierarchical Question Structure**, combining basic and advanced queries; and
-* **Multi-dimensional Evaluation**, assessing the ability of continuous perception, understanding, and reasoning.  -->
+## 👀 $\mathcal{RTV}\text{-}Bench$ Overview
 
+We introduce $\mathcal{RTV}\text{-}Bench$, a fine-grained benchmark for MLLM real-time video analysis, which contains **552** videos (167.2 hours) and **4,631** high-quality QA pairs. We evaluated leading MLLMs, including proprietary (_e.g._ GPT-4o, Gemini 2.0), open-source offline (_e.g._ Qwen2.5-VL, VideoLLaMA3), and open-source real-time (_e.g._ VITA-1.5, InternLM-XComposer2.5-OmniLive) models. Experiment results show open-source real-time models largely outperform offline ones but still trail top proprietary models. Our analysis also reveals that larger model size or higher frame sampling rates do not significantly boost $\mathcal{RTV}\text{-}Bench$ performance, sometimes causing slight decreases. This underscores the need for better model architectures optimized for video stream processing and long sequences to advance real-time video analysis with MLLMs.  $\mathcal{RTV}\text{-}Bench$ includes three key principles: 
+*   **Multi-Timestamp Question Answering (MTQA)**, where answers evolve with scene changes; 
+*   **Hierarchical Question Structure**, combining basic and advanced queries; and
+*   **Multi-dimensional Evaluation**, assessing the ability of continuous perception, understanding, and reasoning. 
 
-<!-- ## 🌟 Star History
+**Video Categories and Distribution of Question Difficulty and Query Characteristics.** 
+<p align="center">
+    <img src="https://github.com/ljungang/rtv-bench/blob/main/asset/2_dataset_stati.png?raw=true" width="100%" height="100%" >
+    (Left) RTV-Bench overs 3 key domains and 16 sub-class video types. 
+    (Center) Distribution of question difficulty levels across eight representative task types, measured by percentage-based performance ranges.
+    (Right) Distribution of question queries by video length, categorized into Shallow, Moderate, and Deep levels. The bar heights indicate counts, while the line chart overlays query proportions for each duration bucket.
+</p>
 
-[![Star History Chart](https://api.star-history.com/svg?repos=LJungang/RTV-Bench&type=Date)](https://star-history.com/#LJungang/RTV-Bench&Date)
+## 🔖Evaluation Results
+<p align="center">
+    <img src="https://github.com/ljungang/rtv-bench/blob/main/asset/3_evaluation.png?raw=true" width="100%" height="100%">
+</p>
 
-If you find our work helpful for your research, please consider citing our work.   
+## 🛠️ Sample Usage (Evaluation)
+To evaluate models on RTV-Bench, you can use the provided script:
+```shell
+bash scripts/eval/eval_model.sh
+```
 
-} -->
+## 📑 Citation
+If you find $\mathcal{RTV}\text{-}Bench$ useful for your research and applications, please cite using this BibTeX:
+```bibtex
+@article{xun2025rtv,
+  title={RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video},
+  author={Xun, Shuhang and Tao, Sicheng and Li, Jungang and Shi, Yibo and Lin, Zhixin and Zhu, Zhanhui and Yan, Yibo and Li, Hanqian and Zhang, Linghao and Wang, Shikang and others},
+  journal={arXiv preprint arXiv:2505.02064},
+  year={2025}
+}
 ```