When Skeleton Meets Appearance: Adaptive Appearance Information Enhancement for Skeleton Based Action Recognition

Suqin Wang, Lu Zhou, Yingying Chen, Jiangtao Huo, Jinqiao Wang

Abstract: Skeleton-based action recognition methods which utilize graph convolution networks (GCNs) have achieved remark-able success in recent years. However, action recognizer can be easily confused by the ambiguity caused by different actions with similar skeleton sequences when only skeleton data is trained. Introducing appearance information can effectively eliminate the ambiguity. Based on this, we introduce a two-stream network for action recognition. One trained on RGB images extracts appearance information. The other trained on skeleton data models motion information and adaptively captures appearance information of action areas at action-related intervals via a specially tailored attention mechanism. Our architecture is trained and evaluated on two large-scale datasets: NTU RGB+D and NTU RGB+D 120, and a small scale human-object interaction dataset Northwestern-UCLA. Experiment results verify the effectiveness of our method and the performance of our method exceeds the state-of-the-art with a significant margin.