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- W2995397798 abstract "Action recognition has been a widely studied topic with a heavy focus on supervised learning involving sufficient labeled videos. However, the problem of cross-domain action recognition, where training and testing videos are drawn from different underlying distributions, remains largely under-explored. Previous methods directly employ techniques for cross-domain image recognition, which tend to suffer from the severe temporal misalignment problem. This paper proposes a Temporal Co-attention Network (TCoN), which matches the distributions of temporally aligned action features between source and target domains using a novel cross-domain co-attention mechanism. Experimental results on three cross-domain action recognition datasets demonstrate that TCoN improves both previous single-domain and cross-domain methods significantly under the cross-domain setting." @default.
- W2995397798 created "2019-12-26" @default.
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- W2995397798 date "2019-12-22" @default.
- W2995397798 modified "2023-10-16" @default.
- W2995397798 title "Adversarial Cross-Domain Action Recognition with Co-Attention" @default.
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- W2995397798 doi "https://doi.org/10.48550/arxiv.1912.10405" @default.
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