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README.md

@@ -1,120 +1,37 @@
-# 🎥 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>
+  <h1 align="center"> ✏️ Data for VidChain Excercise</h1>
+  <h2 align="center">VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning</h2>
+  
+<p align="center">Ji Soo Lee*, Jongha Kim*, Jeehye Na, Jinyoung Park, Hyunwoo J. Kim†.
+  </p>
 
-<div align="center">
-  <img src="asset/main.png" width="750px" alt="VidChain Framework" />
-</div>
+  <h2 align="center">
+    AAAI 2025 
+  </h2>
 
-## 📚 About This Repository
+  <h3 align="center">
+    <a href="https://arxiv.org/pdf/2501.06761" target='_blank'><img src="https://img.shields.io/badge/arXiv-2501.06761-b31b1b.svg"></a>
+    <a href="https://huggingface.co/datasets/simplecloud/VidChain-Data"><img src="https://img.shields.io/badge/huggingface-datasets-yellow"></a>
+  </h3>
 
-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
+<div align="center">
+  <img src="asset/main.png" width="750px" />
+</div>
 
-## 🚀 Learning Objectives
 
+## 🎯 Learning Objectives
 By working through this exercise, you will:
+- Reproduce baseline behavior of a video-language model (**VTimeLLM**, CVPR 2024 Highlight).  
+- Observe the 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 potential strategies for ensembling different reasoning paths of VidChain's CoTasks.  
 
-- ✅ **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
+<br>
 
+## Citations 🌱
 ```
-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},
@@ -122,18 +39,3 @@ If you find this work useful, please cite:
   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>