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Improving Skeleton-based Action Recognitionwith Robust Spatial and Temporal Features
[article]
2020
arXiv
pre-print
Recently skeleton-based action recognition has made signif-icant progresses in the computer vision community. Most state-of-the-art algorithms are based on Graph Convolutional Networks (GCN), andtarget at improving the network structure of the backbone GCN lay-ers. In this paper, we propose a novel mechanism to learn more robustdiscriminative features in space and time. More specifically, we add aDiscriminative Feature Learning (DFL) branch to the last layers of thenetwork to extract
arXiv:2008.00324v1
fatcat:tyiuhmd54nhrfcqg36sbcie6me