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- W4360888002 abstract "In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal Transformers (SPARTAN) approach to Group Activity Recognition (GAR) using unlabeled video data. Given a video, we create local and global Spatio-temporal views with varying spatial patch sizes and frame rates. The proposed self-supervised objective aims to match the features of these contrasting views representing the same video to be consistent with the variations in spatiotemporal domains. To the best of our knowledge, the proposed mechanism is one of the first works to alleviate the weakly supervised setting of GAR using the encoders in video transformers. Furthermore, using the advantage of transformer models, our proposed approach supports long-term relationship modeling along spatio-temporal dimensions. The proposed SPARTAN approach performs well on two group activity recognition benchmarks, including NBA and Volleyball datasets, by surpassing the state-of-the-art results by a significant margin in terms of MCA and MPCA metrics." @default.
- W4360888002 created "2023-03-25" @default.
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- W4360888002 date "2023-03-06" @default.
- W4360888002 modified "2023-10-18" @default.
- W4360888002 title "SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition" @default.
- W4360888002 doi "https://doi.org/10.48550/arxiv.2303.12149" @default.
- W4360888002 hasPublicationYear "2023" @default.
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