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• W28988658 abstract "We propose in this paper a fully automated deep model, which learns to classify human actions without using any prior knowledge. The first step of our scheme, based on the extension of Convolutional Neural Networks to 3D, automatically learns spatio-temporal features. A Recurrent Neural Network is then trained to classify each sequence considering the temporal evolution of the learned features for each timestep. Experimental results on the KTH dataset show that the proposed approach outperforms existing deep models, and gives comparable results with the best related works." @default.
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• W28988658 date "2011-01-01" @default.
• W28988658 modified "2023-10-17" @default.
• W28988658 title "Sequential Deep Learning for Human Action Recognition" @default.
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• W28988658 doi "https://doi.org/10.1007/978-3-642-25446-8_4" @default.
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