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• W2890791758 abstract "We present a new framework for end-to-end classifying actions in video called Spatial-Temporal Attention Model (STAM) which integrates ideas from attention mechanism to better explore spatial and temporal information, simultaneously, in videos. Specifically, spatial attention is used to locate the action-relevant parts within each video frame, temporal attention is used to measure the discrimination of each time step, and jointly learn the video-level representation for final prediction. Experimental results show our model is comparable to state-of-arts. We also visualize the learned models, which makes our model more interpretable and demonstrates the effectiveness of Spatial-Temporal Attention Model." @default.
• W2890791758 created "2018-09-27" @default.
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• W2890791758 date "2018-01-01" @default.
• W2890791758 modified "2023-10-18" @default.
• W2890791758 title "Spatial-Temporal Attention for Action Recognition" @default.
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• W2890791758 doi "https://doi.org/10.1007/978-3-030-00776-8_78" @default.
• W2890791758 hasPublicationYear "2018" @default.
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