title: Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video
    Reasoning
summary: 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.
paper_authors._id: 68419d4b9892f271ddba2ff5
paper_authors.name: Daeun Lee
paper_authors._id: 68419d4b9892f271ddba2ff6
paper_authors.name: Jaehong Yoon
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T17:08:12.857Z
paper_authors._id: 652066649004117947e46ed6
paper_authors.avatarUrl: /avatars/972c97df6f26d2c3d6ce71ec579984bb.svg
paper_authors.fullname: Jaehong Yoon
paper_authors.type: user
paper_authors.user: jaehong31
paper_authors._id: 68419d4b9892f271ddba2ff7
paper_authors.name: Jaemin Cho
paper_authors._id: 68419d4b9892f271ddba2ff8
paper_authors.name: Mohit Bansal
paper_title: Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video
    Reasoning
paper_summary: 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.
paper_ai_summary: Video-SKoT framework improves domain-adaptive video reasoning by constructing skill-aware Chain-of-Thought supervisions and specialized expert modules.
paper_ai_keywords: Chain-of-Thought CoT reasoning skill-aware CoT skill taxonomy multi-step CoT rationale skill-specific expert learning lightweight adapters domain-adaptive video reasoning