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- W2892298058 abstract "This paper considers a 3D convolutional neural network (CNN) that learns spatial and temporal regions of higher importance through a bi-direction long short-term memory (bi-LSTM) attention for action recognition. First- and second-order differences of spatially most relevant C3D features (sp-m-C3D) are obtained, and the concatenation of the two differences with the sp-m-C3D is used to generate a temporal attention on the sp-m-C3D using a bi-LSTM. Temporally most relevant sp-m-C3D features are fed into another bi-LSTM for action recognition. Essentially, the network learns spatial and temporal regions of high importance for action recognition. We evaluate the network on two public action recognition datasets: UCF-101 (YouTube Action) and HMDB51. The proposed network performs better compared to other state-of-the-art networks." @default.
- W2892298058 created "2018-09-27" @default.
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- W2892298058 date "2018-10-01" @default.
- W2892298058 modified "2023-10-16" @default.
- W2892298058 title "Action Recognition: First-and Second-Order 3D Feature in Bi-Directional Attention Network" @default.
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- W2892298058 doi "https://doi.org/10.1109/icip.2018.8451493" @default.
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