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✏️ Data for VidChain Excercise
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 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.
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}
}
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