Graph Instinctive Attention Convolutional Network for Skeleton-Based Action Recognition

Abstract

Graph convolutional networks (GCNs) are widely used in skeleton-based action recognition and have achieved excellent results. However, it is evident that the convolution operation can lead to losing some original input information. The incomplete utilisation of original input data limits GCNs' ability to obtain the skeleton's correlation. This paper proposes a graph instinctive attention convolutional network (GIAN) to solve this problem. In particular, it contains an instinctive attention module that uses self-attention to obtain the correlation within the original input skeleton. Then, parameter attention is used to further refine the relationship between different skeleton joints. Experimental results on publicly available datasets demonstrate that the GIAN outperforms most of the state-of-the-art algorithms.