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• W3159461954 abstract "In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging multi-view complementary supervision signal. CrosSCLR consists of both single-view contrastive learning (SkeletonCLR) and cross-view consistent knowledge mining (CVC-KM) modules, integrated in a collaborative learning manner. It is noted that CVC-KM works in such a way that high-confidence positive/negative samples and their distributions are exchanged among views according to their embedding similarity, ensuring cross-view consistency in terms of contrastive context, i.e., similar distributions. Extensive experiments show that CrosSCLR achieves remarkable action recognition results on NTU-60 and NTU-120 datasets under unsupervised settings, with observed higher-quality action representations. Our code is available at https://github.com/LinguoLi/CrosSCLR." @default.
• W3159461954 created "2021-05-10" @default.
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• W3159461954 date "2021-04-29" @default.
• W3159461954 modified "2023-09-25" @default.
• W3159461954 title "3D Human Action Representation Learning via Cross-View Consistency Pursuit" @default.
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• W3159461954 doi "https://doi.org/10.48550/arxiv.2104.14466" @default.
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