Multi-Part Adaptive Graph Convolutional Network for Skeleton-Based Action Recognition

In skeleton-based action recognition task, graph convolutional network has attracted widespread attention and achieved remarkable results. However, most of the current methods are performing graph convolution on the entire skeleton graph, ignoring the fact that people are composed of different body parts. In addition, previous work ignores the temporal and spatial independence and relevance of different parts. Thus, to solve these issues, we optimize the representation of the skeleton graph, graph convolution and temporal convolution respectively. In this work, we propose multi-part adaptive graph convolution (MPA-GC) to adaptively learn the topology of each part of the body and dynamically aggregate the relevance between them. Meanwhile, we add a multi-scale temporal convolution module to better obtain temporal dimension features. Ultimately, we develop a powerful graph convolutional network named MPA-GCN, and extensive experiments on two public large-scale datasets NTU-RGB+D and NTU-RGB+D120 demonstrate the effectiveness of our module, which outperforms state-of-the-art methods.