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We propose a deep neural network for the prediction of future frames in natural video sequences.
Recognizing human actions: A local svm approach
C. Schuldt, I. Laptev, and B. Caputo · 2004
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Reducing the dimensionality of data with neural networks
G. Hinton and R. Salakhutdinov · 2006
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Actions as space-time shapes
L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri · 2007
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A data-driven approach for event prediction
J. Yuen and A. Torralba · 2010
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Human activity prediction: Early recognition of ongoing activities from streaming videos
M. S. Ryoo · 2011
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Adaptive deconvolutional networks for mid and high level feature learning
M. D. Zeiler, G. W. Taylor, and R. Fergus · 2011
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UCF101: A dataset of 101 human actions classes from videos in the wild
K. Soomro, A. R. Zamir, and M. Shah · 2012
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Max-margin early event detectors
M. Hoai and F. Torre · 2013
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DeepFlow: Large displacement optical flow with deep matching
P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
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A hierarchical representation for future action prediction
T. Lan, T. Chen, and S. Savarese · 2014
Cited alongside, same era.
Seeing the arrow of time
L. C. Pickup, Z. Pan, D. Wei, Y. Shih, C. Zhang, A. Zisserman, B. Scholkopf, and W. T. Freeman · 2014
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Dejavu: Motion prediction in static images
S. L. Pintea, J. C. van Gemert, and A. W. M. Smeulders · 2014
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Video (language) modeling: a baseline for generative models of natural videos
M. Ranzato, A. Szlam, J. Bruna, M. Mathieu, R. Collobert, and S. Chopra · 2014
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Two-stream convolutional networks for action recognition in videos
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Patch to the future: Unsupervised visual prediction
J. Walker, A. Gupta , and M. Hebert · 2014
Action-conditional video prediction using deep networks in atari games
J. Oh, X. Guo, H. Lee, R. L. Lewis, and S. Singh · 2015
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Spatio-temporal video autoencoder with differentiable memory
V. Patraucean, A. Handa, and R. Cipolla · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-k. Wong, and W.-c. WOO · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Unsupervised learning of video representations using lstms
N. Srivastava, E. Mansimov, and R. Salakhudinov · 2015
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Learning to linearize under uncertainty
R. Goroshin, M. Mathieu, and Y. LeCun · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Unsupervised learning of visual structure using predictive generative networks
W. Lotter, G. Kreiman, and D. Cox · 2015
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Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2015
Cited alongside, same era.
C. Vondrick, H. Pirsiavash, and A. Torralba · 2015
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Unsupervised learning for physical interaction through video prediction
C. Finn, I. J. Goodfellow, and S. Levine · 2016
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Generating videos with scene dynamics
C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
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An uncertain future: Forecasting from static images using variational autoencoders
J. Walker, C. Doersch, A. Gupta, and M. Hebert · 2016
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Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks
T. Xue, J. Wu, K. L. Bouman, and W. T. Freeman · 2016
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