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README.md

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-license: mit
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+# 🎥 VidChain Exercise: Chain-of-Tasks with Metric-based Direct Preference Optimization
+
+<p align="center">
+  <img src="https://img.shields.io/badge/AAAI-2025-blue" alt="AAAI 2025">
+  <a href="https://arxiv.org/pdf/2501.06761" target='_blank'><img src="https://img.shields.io/badge/arXiv-2501.06761-b31b1b.svg" alt="arXiv"></a>
+  <a href="https://huggingface.co/datasets/simplecloud/VidChain-Data"><img src="https://img.shields.io/badge/huggingface-datasets-yellow" alt="Hugging Face Dataset"></a>
+</p>
+
+<div align="center">
+  <img src="asset/main.png" width="750px" alt="VidChain Framework" />
+</div>
+
+## 📚 About This Repository
+
+This repository contains the exercise materials and implementation for **VidChain**, a novel framework for Dense Video Captioning with VideoLLMs. VidChain combines Chain-of-Tasks (CoTasks) and Metric-based Direct Preference Optimization (M-DPO) to achieve superior temporal reasoning and coherence in video understanding.
+
+### 🎯 Research Paper
+**VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning**  
+*Ji Soo Lee*, Jongha Kim*, Jeehye Na, Jinyoung Park, Hyunwoo J. Kim†  
+AAAI 2025
+
+## 🚀 Learning Objectives
+
+By working through this exercise, you will:
+
+- ✅ **Reproduce baseline behavior** of a video-language model (**VTimeLLM**, CVPR 2024 Highlight)
+- 🔍 **Observe limitations** of existing approaches in temporal reasoning and coherence
+- 🛠️ **Implement and experiment** with VidChain's improvements using M-DPO
+- 🎬 **Run inference** on videos to generate dense temporal captions (Dense Video Captioning)
+- 📊 **Evaluate** how preference alignment improves performance over baselines
+- 💡 **Discuss strategies** for ensembling different reasoning paths of VidChain's CoTasks
+
+## 📁 Repository Structure
+
+```
+VidChain-exercise/
+├── README_HF.md              # This file - Hugging Face README
+├── READ.md                   # Original exercise README
+├── upload.py                 # Upload script for Hugging Face Hub
+├── upload_single_file.py     # Single file upload utility
+├── remove_file.py           # File removal utility
+├── setup_hf_upload.py       # Setup script for HF upload
+├── HF_UPLOAD_GUIDE.md       # Comprehensive upload guide
+├── requirements.txt         # Python dependencies
+├── app.py                   # Streamlit app for HF Spaces
+├── asset/                   # Project assets and images
+│   └── main.png            # Main framework diagram
+└── VTimeLLM/               # VideoLLM implementation
+    └── ...                 # VTimeLLM source code
+```
+
+## 🔧 Quick Start
+
+### 1. Install Dependencies
+```bash
+pip install -r requirements.txt
+```
+
+### 2. Setup for VideoLLaMA2
+```bash
+# Clone the main repository
+git clone https://github.com/mlvlab/VidChain.git
+cd VidChain
+
+# Install VideoLLaMA2 dependencies
+conda create -n videollama python=3.10 -y
+conda activate videollama
+pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121
+
+cd VideoLLaMA2
+pip install -r requirements.txt
+pip install num2words datasets pycocoevalcap rich
+pip install flash-attn==2.5.7 --no-build-isolation
+```
+
+### 3. Download Pre-trained Models
+- **VideoLLaMA2 checkpoints**: [Download from official repo](https://github.com/DAMO-NLP-SG/VideoLLaMA2?tab=readme-ov-file#earth_americas-model-zoo)
+- **VidChain checkpoints**: [Download from Hugging Face](https://huggingface.co/datasets/simplecloud/VidChain-Data)
+
+## 🎯 Key Features
+
+### Chain-of-Tasks (CoTasks)
+- Novel approach to video understanding through task decomposition
+- Improves temporal reasoning capabilities
+- Enhanced coherence in video captioning
+
+### Metric-based Direct Preference Optimization (M-DPO)
+- Advanced training methodology for preference alignment
+- Better performance over traditional baselines
+- Improved temporal consistency
+
+### VideoLLaMA2 Integration
+- State-of-the-art video-language model
+- Support for ActivityNet and YouCook2 datasets
+- Pre-extracted features for efficient training
+
+## 📊 Dataset Support
+
+This exercise supports two major datasets:
+
+1. **ActivityNet** (301GB pre-extracted features)
+2. **YouCook2** (32GB pre-extracted features)
+
+⚠️ **Storage Warning**: The pre-extracted features require significant storage space. Please ensure you have adequate disk space.
+
+## 🔗 Related Resources
+
+- **Paper**: [VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning](https://arxiv.org/pdf/2501.06761)
+- **Main Repository**: [mlvlab/VidChain](https://github.com/mlvlab/VidChain)
+- **Dataset**: [simplecloud/VidChain-Data](https://huggingface.co/datasets/simplecloud/VidChain-Data)
+- **VideoLLaMA2**: [DAMO-NLP-SG/VideoLLaMA2](https://github.com/DAMO-NLP-SG/VideoLLaMA2)
+
+## 📝 Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@inproceedings{lee2025vidchain,
+  title={VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning},
+  author={Lee, Ji Soo and Kim, Jongha and Na, Jeehye and Park, Jinyoung and Kim, Hyunwoo J},
+  booktitle={AAAI},
+  year={2025}
+}
+```
+
+## 🤝 Contributing
+
+This is an exercise repository for educational purposes. For contributions to the main VidChain project, please visit the [main repository](https://github.com/mlvlab/VidChain).
+
+## 📄 License
+
+This project is released under the same license as the main VidChain repository. Please refer to the main repository for license details.
+
+---
+
+<div align="center">
+  <p><em>Built with ❤️ for the AI research community</em></p>
+  <p><em>Part of the VidChain research project at AAAI 2025</em></p>
+</div>