SOURCE-LINKED INTELLIGENCE
CausalChapter: Improving Long-Video Chaptering with Interventional Dependency Modeling
Long-form instructional videos require automatic chaptering to support browsing, navigation, and knowledge access. Recent long-context language models can perform chaptering from textualized video inputs, but they remain costly and brittle for content-dense lecture videos with long transcripts, smooth topic transitions, and detailed chapter outputs. A scalable segment-then-caption paradigm reduces this cost, but introduces two new challenges: boundary error propagation and fragmented cross-chapter context. We propose \textbf{CausalChapter}, an intervention-inspired framework for long-video cha
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-08T12:54:57.000Z
First collected: 2026-09-20T20:02:11.508Z. This is not the publication date.