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Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
[article]
2019
arXiv
pre-print
Human action recognition from skeleton data, fueled by the Graph Convolutional Network (GCN), has attracted lots of attention, due to its powerful capability of modeling non-Euclidean structure data. However, many existing GCN methods provide a pre-defined graph and fix it through the entire network, which can loss implicit joint correlations. Besides, the mainstream spectral GCN is approximated by one-order hop, thus higher-order connections are not well involved. Therefore, huge efforts are
arXiv:1911.04131v1
fatcat:ml6fl66sgnbipdkugnyrxtinqe