Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video
  Reasoning
Authors: name: Daeun Lee, name: Jaehong Yoon, name: Jaemin Cho, name: Mohit Bansal

Recent advances in Chain-of-Thought (CoT) reasoning have improved complex
video understanding, but existing methods often struggle to adapt to
domain-specific skills (e.g., event detection, spatial relation understanding,
emotion understanding) over various video content. To address this, we propose
Video-Skill-CoT (a.k.a. Video-SKoT), a framework that automatically constructs
and leverages skill-aware CoT supervisions for domain-adaptive video reasoning.
First, we construct skill-based CoT annotations: we extract domain-relevant
reasoning skills from training questions, cluster them into a shared skill
taxonomy, and create detailed multi-step CoT rationale tailored to each
video-question pair for training. Second, we introduce a skill-specific expert
learning framework. Each expert module specializes in a subset of reasoning
skills and is trained with lightweight adapters using the collected CoT
supervision. We demonstrate the effectiveness of the proposed approach on three
video understanding benchmarks, where Video-SKoT consistently outperforms
strong baselines. We also provide in-depth analyses on comparing different CoT
annotation pipelines and learned skills over multiple video domains.