A Local-to-Global Approach to
Multi-modal Movie Scene Segmentation

Anyi Rao      Linning Xu      Yu Xiong      Guodong Xu      Qingqiu Huang      Bolei Zhou      Dahua Lin
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020

Overview


Scene, as the crucial unit of storytelling in movies, contains complex activities of actors and their interactions in a physical location. Identifying the composition and semantics of scenes serves as a critical step towards visual understanding of complex and long videos such as movies, TV episodes, entertainment shows and variety shows.

This work is going to help divide a long video into semantic continuous short videos and output a structural representation. And it also provides research opportunities towards story/plot understanding in long videos with a semantic unit.

Potential Application

Human Interaction Graph Generation

The first line is American Hustle scene segmentation coming from scene detection, where dark blue and light blue intertwine with each other to represent different Scenes. The second to fourth line corresponds to C.B. Christian Bale; B.C. Bradley Cooper; A.A. Amy Adams Scenes occurrence time lines in this movie respectively, where the dark green means occurring while light green does not. The graph below represents their interaction over the story line. The dark red represents a closer relationship while the light red represents a far-away relationship and two demo pictures are shown for closer relationships.

Cross Movie Scene Retrieval

Given a specific scene in one movie and we retrieve similar ones in other movies. For example, we choose a conversation scene and a party scene from Ted (2012) as query, then retrieved scenes from other movies.

Materials


Code and Models


Datasets



Datasets

Citation

@inproceedings{rao2020local,
title={A Local-to-Global Approach to Multi-modal Movie Scene Segmentation},
author={Rao, Anyi and Xu, Linning and Xiong, Yu and Xu, Guodong and Huang, Qingqiu and Zhou, Bolei and Lin, Dahua},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2020}
}