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Improved GCN Framework for Human Motion Recognition
2022
Scientific Programming
Human recognition models based on spatial-temporal graph convolutional neural networks have been gradually developed, and we present an improved spatial-temporal graph convolutional neural network to solve the problems of the high number of parameters and low accuracy of this type of model. The method mainly draws on the inception structure. First, the tensor rotation is added to the graph convolution layer to realize the conversion between graph node dimension and channel dimension and enhance
doi:10.1155/2022/2721618
doaj:bc25bb3130c1468a89bf6e1c62af3607
fatcat:alds5mvnvva4pj74rbxjxlxoyu