Multi-modal Video Chapter Generation

TL;DR

This paper introduces a new dataset and a multi-modal framework for automatic video chapter generation, leveraging visual and narration features to improve localization and titling of chapters in online videos.

Contribution

The paper presents the first large-scale dataset for video chapter generation and a novel multi-modal model that effectively combines visual and narration cues for this task.

Findings

Proposed method outperforms existing approaches.

Skip sliding window effectively localizes chapters.

Multi-modal fusion improves title generation accuracy.

Abstract

Chapter generation becomes practical technique for online videos nowadays. The chapter breakpoints enable users to quickly find the parts they want and get the summative annotations. However, there is no public method and dataset for this task. To facilitate the research along this direction, we introduce a new dataset called Chapter-Gen, which consists of approximately 10k user-generated videos with annotated chapter information. Our data collection procedure is fast, scalable and does not require any additional manual annotation. On top of this dataset, we design an effective baseline specificlly for video chapters generation task. which captures two aspects of a video,including visual dynamics and narration text. It disentangles local and global video features for localization and title generation respectively. To parse the long video efficiently, a skip sliding window mechanism is designed to localize potential chapters. And a cross attention multi-modal fusion module is developed to aggregate local features for title generation. Our experiments demonstrate that the proposed framework achieves superior results over existing methods which illustrate that the method design for similar task cannot be transfered directly even after fine-tuning. Code and dataset are available at https://github.com/czt117/MVCG.