Create README.md

README.md

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+---
+license: mit
+task_categories:
+- video-text-to-text
+language:
+- en
+size_categories:
+- 1K<n<10K
+---
+<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) [![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. You can find the RTV-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).
+
+## TODO
+- [ ] Release the final label json.
+- [ ] Release the evaluation code.
+- [ ] Construct a more comprehensive benchmark for real-time video analysis.
+- [ ] ···
+## 👀 $\mathcal{RTV}\text{-}Bench$ 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. 
+
+
+## 🌟 Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=LJungang/RTV-Bench&type=Date)](https://star-history.com/#LJungang/RTV-Bench&Date)
+
+If you find our work helpful for your research, please consider citing our work.   
+
+```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}
+}
+```