Self-Attention Enhanced Graph Convolution Network for 3D Skeleton Based Human Action Recognition
Existing methods based on standard graph convolutional networks mainly rely on local graph convolution operations,limiting their flexibility in capturing complex long-range associations between joints.To address these issues,a Self-Attention Enhanced Graph Convolutional Network(SGNet)is proposed.Leveraging the characteristics of skeletal data,independent global modeling is performed for each channel related to key points,specifically termed Channel-Specific Global Spatial Modeling(C-GSM).This is carried out in parallel with a local spatial modeling(LSM)to extract local and glob-al spatial feature representations.Extensive experimental research was conducted on two large and challenging benchmark datasets,NTU RGB+D and NTU RGB+D120.SGNet demonstrated highly competitive results in comparison with the state-of-the-art methods,achieving the highest accuracy rates of 92.9%on NTU RGB+D X-Sub and 90.7%on NTU RGB+D 120 X-Set,respectively.
skeleton-based human action recognitiongraph convolution networkself-attention
NETL
NSTL
万方数据
