Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video
  Reasoning
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.
68419d4b9892f271ddba2ff5
Daeun Lee
68419d4b9892f271ddba2ff6
Jaehong Yoon
claimed_verified
2025-06-05T17:08:12.857Z
652066649004117947e46ed6
/avatars/972c97df6f26d2c3d6ce71ec579984bb.svg
Jaehong Yoon
user
jaehong31
68419d4b9892f271ddba2ff7
Jaemin Cho
claimed_verified
2025-07-11T16:32:05.249Z
5ffe32d8942cf3533d364449
https://cdn-avatars.huggingface.co/v1/production/uploads/1654821969191-5ffe32d8942cf3533d364449.jpeg
Jaemin Cho
user
j-min
68419d4b9892f271ddba2ff8
Mohit Bansal
Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video
  Reasoning
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.
Video-SKoT framework improves domain-adaptive video reasoning by constructing skill-aware Chain-of-Thought supervisions and specialized expert modules.
Chain-of-Thought CoT reasoning skill-aware CoT skill taxonomy multi-step CoT rationale skill-specific expert learning lightweight adapters domain-adaptive video reasoning