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We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos.
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Adam: A method for stochastic optimization
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M. Koperski, P. Bilinski, and F. Bremond · 2014
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C. Lu, J. Jia, and C.-K. Tang · 2014
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H. Rahmani, A. Mahmood, D. Q. Huynh, and A. Mian · 2014
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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N. Srivastava, E. Mansimov, and R. Salakhutdinov · 2015
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Dense optical flow prediction from a static image
J. Walker, A. Gupta, and M. Hebert · 2015
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L. Wang, Y. Qiao, and X. Tang · 2015
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Very deep convolutional networks for large-scale image recognition
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I. Sutskever, O. Vinyals, and Q. V. Le · 2014
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Super normal vector for activity recognition using depth sequences
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Discriminative orderlet mining for real-time recognition of human-object interaction
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Towards good practices for very deep two-stream convnets
L. Wang, Y. Xiong, Z. Wang, and Y. Qiao · 2015
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Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
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Watch-n-patch: Unsupervised understanding of actions and relations
C. Wu, J. Zhang, S. Savarese, and A. Saxena · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
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Beyond short snippets: Deep networks for video classification
J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici · 2015
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Generating videos with scene dynamics
C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
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R. Zhang, P. Isola, and A. A. Efros · 2016
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