Temporal Scene Montage for Self-Supervised Video Scene Boundary Detection

Abstract

Once a video sequence is organized as basic shot units, it is of great interest to temporally link shots into semantic-compact scene segments to facilitate long video understanding. However, it still challenges existing video scene boundary detection methods to handle various visual semantics and complex shot relations in video scenes. We proposed a novel self-supervised learning method, Video Scene Montage for Boundary Detection (VSMBD), to extract rich shot semantics and learn shot relations using unlabeled videos. More specifically, we present Video Scene Montage (VSM) to synthesize reliable pseudo scene boundaries, which learns task-related semantic relations between shots in a self-supervised manner. To lay a solid foundation for modeling semantic relations between shots, we decouple visual semantics of shots into foreground and background. Instead of costly learning from scratch as in most previous self-supervised learning methods, we build our model upon large-scale pre-trained visual encoders to extract the foreground and background features. Experimental results demonstrate VSMBD trains a model with strong capability in capturing shot relations, surpassing previous methods by significant margins. The code is available at https://github.com/mini-mind/VSMBD.