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• W3164678500 startingPage "108043" @default.
• W3164678500 abstract "Arbitrary-view human action recognition is still a big challenge due to the view changes. A possible solution is to enlarge the view range of action samples in the training set. Therefore, we propose a Two-Branch Novel-View action Generation approach based on auxiliary conditional GAN, which generates a novel-view action sample for arbitrary-view human action recognition. The generated sample enlarge the view range of action samples for training. Furthermore, to narrow the representation of actions in different views, we propose a view-domain generalization model that improves the recognition performance of arbitrary-view human action recognition. Our approach is evaluated on three large-scale RGB+D skeleton datasets including UESTC varying-view RGB+D dataset, NTU RGB+D 60, and NTU RGB+D 120 datasets, with two types of view-invariant evaluations, i.e., the cross-view, and arbitrary-view recognition. The proposed approach achieves outstanding performance in human action recognition." @default.
• W3164678500 created "2021-06-07" @default.
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• W3164678500 date "2021-10-01" @default.
• W3164678500 modified "2023-10-10" @default.
• W3164678500 title "Arbitrary-view human action recognition via novel-view action generation" @default.
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• W3164678500 doi "https://doi.org/10.1016/j.patcog.2021.108043" @default.
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