✏️ 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}
}