Hierarchical graph attention network with pseudo-metapath for skeleton-based action recognition

Abstract

Skeleton-based action recognition has gained significant attention in computer vision. Most state-of-the-art (SOTA) approaches view the skeleton as a homogeneous graph. Unlike those approaches, this paper shows that methods in the heterogeneous graph manner can also achieve competitive performance. In this paper, a logical heterogeneous skeleton graph is built under the assumption of the heterogeneity of joints and bones at different positions, and the action recognition task is formulated as message aggregation and prediction on this heterogeneous graph. Specifically, a novel semantic concept named pseudo-metapath is introduced to represent dependencies between joints, based on which a hierarchical graph attention network with the joint-level attention and the semantic-level attention modules is proposed to capture richer skeleton features. The joint-level attention module intends to get the local difference among the joints within each pseudo-metapath, while the semantic-level attention module is capable of learning the global importance of different pseudo-metapaths. Extensive experiments on the NTU-RGB + D 60, NTU-RGB + D 120 and the SYSU datasets, validate that our model can attain comparable performance to the SOTA methods with 15x fewer input frames, 26.3x less FLOPs and 2.8x less parameters.