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      Trear: Transformer-Based RGB-D Egocentric Action Recognition

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          Most cited references28

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          Attention Is All You Need

          The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. 15 pages, 5 figures
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            Learning realistic human actions from movies

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              Two-stream convolutional networks for action recognition in videos

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                Author and article information

                Contributors
                Journal
                IEEE Transactions on Cognitive and Developmental Systems
                IEEE Trans. Cogn. Dev. Syst.
                Institute of Electrical and Electronics Engineers (IEEE)
                2379-8920
                2379-8939
                March 2022
                March 2022
                : 14
                : 1
                : 246-252
                Affiliations
                [1 ]School of Electrical and Information Engineering, Tianjing University, Tianjin, China
                [2 ]Alibaba Group, Bellevue, WA, USA
                [3 ]School of Information Engineering, Zhengzhou University, Zhengzhou, China
                [4 ]Advanced Multimedia Research Laboratory, University of Wollongong, Wollongong, NSW, Australia
                Article
                10.1109/TCDS.2020.3048883
                634d8692-acce-4eab-8879-5a8694be5b59
                © 2022

                https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-037

                History

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