Upload README.md

README.md

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-license: apache-2.0
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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
+
+
+H²VU-Benchmark is designed to comprehensively assess the capabilities of video understanding models, particularly in real-world scenarios. It addresses limitations of existing benchmarks by focusing on **extended video durations, advanced task complexity, and diversified real-world data.**
+
+## Key Features
+
+*   **Three-Tier Hierarchical Competency Classification:** (L-1 to L-3) with **10,183 evaluation tasks** covering a broad spectrum of diverse data.
+*   **Two Main Categories:**
+    *   **Offline General Video:** Employs common perception and reasoning tasks, along with novel tasks focusing on **countercommonsense comprehension** and **trajectory tracking.** (27 evaluation task types)
+    *   **Online Streaming Video:** Utilizes standard perception and reasoning tasks. (20 evaluation task types)
+
+## Key Differentiators from Existing Benchmarks
+
+Our work distinguishes itself through three key features:
+
+*   **Extended Video Duration:**
+    *   Encompasses a diverse range from a **few seconds to 1.5 hours**, significantly expanding the temporal scope.
+    *   Evaluates models' ability to capture **short-term dynamics** and model **long-term dependencies.**
+*   **Advanced Task Complexity:**
+    *   Builds on traditional perceptual and reasoning tasks with the introduction of two new modules:
+        *   **Counterfactual Reasoning:** Assesses vision-oriented understanding through tasks that defy common sense (e.g., implausible causal relationships).
+        *   **Trajectory State Tracking:** Evaluates the ability to track and predict the states and trajectories of targets in complex dynamic scenes.
+*   **Diversified Real-World Data:**
+    *   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)
+
+