| <p align="center"> |
| <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> |
|
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| <h2 align="center"> |
| AAAI 2025 |
| </h2> |
| |
| <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> |
| |
| <div align="center"> |
| <img src="asset/main.png" width="750px" /> |
| </div> |
|
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|
|
| ## 🎯 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. |
|
|
| <br> |
|
|
| ## Citations 🌱 |
| ``` |
| @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} |
| } |
| ``` |
|
|
