Semantics-guided Adaptive Topology Inference Graph Convolutional Networks for Skeleton-based Action Recognition

Graph convolutional networks (GCN), with natural advantages for skeleton-based action recognition, has attracted more and more attention. The key lies in how to obtain richer feature information and the design of the skeleton topology. In this research, the feature fusion method of joint and semantics (joint type and frame index) is improved, and integrated into a Semantics Coding Module (SCM), which is more applicable for complex multi-layer networks. Guided by the SCM, the network can obtain more feature information of skeleton. Secondly, a skeleton Topology Inference Network (TIN) is proposed, which adaptively learns different adjacency matrices according to the context information of different samples with the efficient feature learning ability of CNN, so that the network can get rid of the limitation of fixed topology. By applying the SCM and TIN to 2s-AGCN, we propose a semantics-guided multi-stream adaptive topology inference graph convolutional network for skeleton-based action recognition. Extensive experiments on datasets, NTU RGB+D and NTU RGB+D 120, demonstrate that our methods obviously improve the accuracy of network and our model has achieved the state-of-the-art performance.