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• W2788332574 abstract "In this paper, we propose an efficient and straightforward approach, video you only look once (VideoYOLO), to capture the overall temporal dynamics from an entire video in a single process for action recognition. It remains an open question for action recognition on how to deal with the temporal dimension in videos. Existing methods subdivide a whole video into either individual frames or short clips and consequently have to process these fractions multiple times. A post process is then used to aggregate the partial dynamic cues to implicitly infer the whole temporal information. On the contrary, in VideoYOLO, we first generate a proxy video by selecting a subset of frames to roughly reserve the overall temporal dynamics presented in the original video. A 3D convolutional neural network (3D-CNN) is employed to learn the overall temporal characteristics from the proxy video and predict action category in a single process. Our proposed method is extremely fast. VideoYOLO-32 is able to process 36 videos per second that is 10 times and 7 times faster than prior 2D-CNN (Two-stream (Simonyan and Zisserman, 2014)) and 3D-CNN (C3D (Tran et al., 2015)) based models, respectively, while still achieves superior or comparable classification accuracies on the benchmark datasets, UCF101 and HMDB51." @default.
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• W2788332574 date "2018-04-01" @default.
• W2788332574 modified "2023-10-18" @default.
• W2788332574 title "Video you only look once: Overall temporal convolutions for action recognition" @default.
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• W2788332574 doi "https://doi.org/10.1016/j.jvcir.2018.01.016" @default.
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