Update README.md

README.md

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 # H2VU-Benchmark: A Comprehensive Benchmark for Hierarchical Holistic Video Understanding
 
-![VideoQA](https://img.shields.io/badge/Task-VideoQA-red) 
-![Multi-Modal](https://img.shields.io/badge/Task-Multi--Modal-red) 
-![H2VU](https://img.shields.io/badge/Dataset-H2VU-blue)  
-![Gemini](https://img.shields.io/badge/Model-Gemini-green) 
-![GPT-4V](https://img.shields.io/badge/Model-GPT--4V-green) 
-![GPT-4o](https://img.shields.io/badge/Model-GPT--4o-green)
-
-
-<font size=7><div align='center' > [[🍎 Project Page](/)] [[📖 arXiv Paper]()] [[📊 Dataset]()]   </div></font>
-
-H2VU-Benchmark applies to  **video MLLMs**. 🌟
-
----
-
-## 🔥 Updates News
-
-
-* **`2024.04.30`** 🌟 We have open-sourced our dataset on Hugging Face.
-* **`2025.04.01`** 🌟 We are very proud to launch H2VU-Benchmark, the first comprehensive evaluation benchmark for Multimodal Large Language Models (MLLMs) in offline general video and online streaming analysis!
-
 
 
 ## 👀 H²VU-Benchmark Overview
@@ -49,72 +29,4 @@ Our work distinguishes itself through three key features:
     *   Incorporates **first-person streaming video data** to better simulate real-world streaming data processing needs.
     *   Explores multimodal models' performance in understanding first-person streaming video, crucial for AI agents functioning as real-world assistants or autonomous agents.
 
-<p align="center">
-    <img src="./asset/sta.jpg" width="100%" height="100%">
-</p>
-
-## 📐 Dataset Examples
-
-<p align="center">
-    <img src="./asset/Highlights-2.png" width="100%" height="100%">
-</p>
-
-
-## 🔍 Dataset
-
-**License**:
-```
-H²VU-Benchmark is only used for academic research. Commercial use in any form is prohibited.
-The copyright of all videos belongs to the video owners.
-If there is any infringement in H²VU-Benchmark, please email us and we will remove it immediately.
-Without prior approval, you cannot distribute, publish, copy, disseminate, or modify H²VU-Benchmark in whole or in part. 
-You must strictly comply with the above restrictions.
-```
-
-
-
-## 🔮 Evaluation Pipeline
-
-
-📍 **Prompt**:
-
-The common prompt used in our evaluation follows this format:
-
-```
-Select the best answer to the following multiple-choice question based on the video. Respond with only the letter (A, B, C, or D) of the correct option. 
-[Question]
-The best answer is:
-```
-
-📍 **Evaluation**: 
-
-To extract the answer and calculate the scores, we add the model response to a JSON file. Here we provide an example template [output.json](./evaluation/output.json). Once you have prepared the model responses in this format, please refer to the evaluation script [eval_results.py](./evaluation/eval_results.py), and you will get the accuracy scores across video_durations, video domains, video subcategories, and task types. 
-The evaluation does not introduce any third-party models, such as ChatGPT.
-
-```bash
-python eval_results.py 
-```
-
-
-## :black_nib: Citation
-
-If you find our work helpful for your research, please consider citing our work.   
-
-```bibtex
-@article{2025h2vu,
-  title={H2VU-Benchmark: A Comprehensive Benchmark for Hierarchical Holistic Video Understanding},
-  author={Wu, Qi and Zheng, Quanlong and Zhang, Yanhao and Xie, Junlin and Luo, Jinguo and Wang, Kuo and Liu, Peng and Xie, Qingsong and Zhen, Ru and Lu, Haonan and others},
-  journal={arXiv preprint arXiv:2503.24008},
-  year={2025}
-}
-```
-
-## 📜 Related Works
-
-Explore our related researches:
-
--  **[MME]** [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://arxiv.org/pdf/2306.13394)
-
--  **[MME-Survey]** [MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs](https://arxiv.org/pdf/2411.15296)
-