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CNN-based optical flow estimation has attracted attention recently, mainly due to its impressively high frame rates.
Determining optical flow
B. K. P. Horn and B. G. Schunck · 1981
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Multitask Learning
R. Caruana · 1998
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High Accuracy Optical Flow Estimation Based on a Theory for Warping
T. Brox, A. Bruhn, N. Papenberg, and J. Weickert · 2004
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A large video database for human motion recognition
H. Jhuang, H. Garrote, E. Poggio, T. Serre, and T. Hmdb · 2011
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A naturalistic open source movie for optical flow evaluation
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black · 2012
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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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TV-L1 optical flow estimation
J. S. Pérez, E. Meinhardt-Llopis, and G. Facciolo · 2013
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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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Déjà Vu:
S. L. Pintea, J. C. van Gemert, and A. W. M. Smeulders · 2014
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FlowNet: Learning Optical Flow With Convolutional Networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox · 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.
Spatio-temporal video autoencoder with differentiable memory
V. Patraucean, A. Handa, and R. Cipolla · 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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Dense Optical Flow Prediction from a Static Image
J. Walker, A. Gupta, and M. Hebert · 2015
Cited alongside, same era.
Unsupervised convolutional neural networks for motion estimation
A. Ahmadi and I. Patras · 2016
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N. Mayer, E. Ilg, P. Hausser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
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Temporal Generative Adversarial Nets
M. Saito and E. Matsumoto · 2016
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Learning to Extract Motion from Videos in Convolutional Neural Networks
D. Teney and M. Hebert · 2016
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Deep End2End Voxel2Voxel Prediction
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 2016
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Anticipating Visual Representations From Unlabeled Video
C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
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Unsupervised learning for physical interaction through video prediction
C. Finn, I. Goodfellow, and S. Levine · 2016
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Look-Ahead Before You Leap: End-to-End Active Recognition by Forecasting the Effect of Motion
D. Jayaraman and K. Grauman · 2016
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Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
W. Lotter, G. Kreiman, and D. Cox · 2016
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Geometry-Based Next Frame Prediction from Monocular Video
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The Cityscapes Dataset for Semantic Urban Scene Understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele
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Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite
A. Geiger, P. Lenz, and R. Urtasun
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Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba
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J. Walker, C. Doersch, A. Gupta, and M. Hebert · 2016
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Temporal segment networks: Towards good practices for deep action recognition
L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. Van Gool · 2016
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Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
T. Xue, J. Wu, K. Bouman, and B. 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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