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We address the problem of synthesizing new video frames in an existing video, either in-between existing frames (interpolation), or subsequent to them (extrapolation).
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Are we ready for autonomous driving? the kitti vision benchmark suite
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Imagenet classification with deep convolutional neural networks
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UCF101: A dataset of 101 human actions classes from videos in the wild
K. Soomro, A. Roshan Zamir, and M. Shah · 2012
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Generative adversarial nets
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Large-scale video classification with convolutional neural networks
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Fast burst images denoising
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Video (language) modeling: a baseline for generative models of natural videos
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Two-stream convolutional networks for action recognition in videos
K. Simonyan and A. Zisserman · 2014
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Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Hazirbas, V. Golkov, P. v.d. Smagt, D. Cremers, and T. Brox · 2015
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Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischery, E. Ilg, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, T. Brox, et al · 2015
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THUMOS challenge: Action recognition with a large number of classes, 2015
A. Gorban, H. Idrees, Y.-G. Jiang, A. Roshan Zamir, I. Laptev, M. Shah, and R. Sukthankar · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
Deepwarp: Photorealistic image resynthesis for gaze manipulation
Y. Ganin, D. Kononenko, D. Sungatullina, and V. Lempitsky · 2016
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Learning image matching by simply watching video
G. Long, L. Kneip, J. M. Alvarez, H. Li, X. Zhang, and Q. Yu · 2016
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Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2016
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Shuffle and learn: unsupervised learning using temporal order verification
I. Misra, C. L. Zitnick, and M. Hebert · 2016
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Multi-view 3d models from single images with a convolutional network
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2016
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Generating Videos with Scene Dynamics
C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
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Phase-based frame interpolation for video
S. Meyer, O. Wang, H. Zimmer, M. Grosse, and A. Sorkine-Hornung · 2015
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Epicflow: Edge-preserving interpolation of correspondences for optical flow
J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid · 2015
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Unsupervised learning of video representations using lstms
N. Srivastava, E. Mansimov, and R. Salakhutdinov · 2015
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Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
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Unsupervised learning for physical interaction through video prediction
C. Finn, I. Goodfellow, and S. Levine · 2016
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Deepstereo: Learning to predict new views from the world’s imagery
J. Flynn, I. Neulander, J. Philbin, and N. Snavely · 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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Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks
J. Xie, R. B. Girshick, and A. Farhadi · 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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Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness
J. J. Yu, A. W. Harley, and K. G. Derpanis · 2016
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View synthesis by appearance flow
T. Zhou, S. Tulsiani, W. Sun, J. Malik, and A. A. Efros · 2016
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Deep view morphing
D. Ji, J. Kwon, M. McFarland, and S. Savarese · 2017
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