Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning

Abstract

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.