Reproduce “Multi-modal Video Chapter Generation” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Xiao Cao, Zitan Chen, Canyu Le, Lei Meng
Researchers from Shandong University and ByteDance introduce the new task of multi-modal video chapter generation, creating a benchmark dataset called ChapterGen and a two-stage framework. This framework automatically segments long online videos into meaningful chapters and generates descriptive titles for each, leveraging both visual dynamics and narration text to outperform existing video understanding methods.
Key insights
- Video chapter generation is a distinct high-level task, as existing video understanding methods perform poorly when adapted to it.
- Narration text provides highly discriminative features for both chapter localization and title generation, offering significant complementary information to visual cues.
- Sophisticated multi-modal fusion mechanisms, such as cross-attention, are crucial for effectively combining visual and textual information to achieve superior performance over simpler fusion methods.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.