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This paper shows how to extract dense optical flow from videos with a convolutional neural network (CNN).
Determining optical flow
B. Horn and S. B · 1981
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Spatiotemporal energy models for the perception of motion
E. H. Adelson and J. Bergen · 1985
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Model for the extraction of image flow
D. J. Heeger · 1987
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Computation of component image velocity from local phase information
D. Fleet and A. Jepson · 1990
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Fitting models to distributed representations of vision
S. A. Niyogi · 1995
Earlier work this paper cites.
A multigrid approach for hierarchical motion estimation
M. E. and P. P · 1998
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Efficient backprop
Y. LeCun, L. Bottou, G. Orr, and K. Muller · 1998
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Rotation invariant neural network-based face detection
H. Rowley, S. Baluja, and T. Kanade · 1998
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Learning sparse, overcomplete representations of time-varying natural images
B. Olshausen · 2003
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High accuracy optical flow estimation based on a theory for warping
T. Brox, A. Bruhn, N. Papenberg, and W. J · 2004
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Rotation-invariant neoperceptron
B. Fasel and D. Gatica-Perez · 2006
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How mt cells analyze the motion of visual patterns
N. C. Rust, V. Mante, E. P. Simoncelli, and J. A. Movshon · 2006
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A database and evaluation methodology for optical flow
S. Baker, D. Scharstein, J. Lewis, S. Roth, M. J. Black, and S. R · 2007
Earlier work this paper cites.
The structure of multiplicative motions in natural imagery
K. G. Derpanis and R. P. Wildes · 2010
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Tiled convolutional neural networks
Q. V. Le, J. Ngiam, Z. Chen, D. Chia, P. W. Koh, and A. Y. Ng · 2010
Cited alongside, same era.
Secrets of optical flow estimation and their principles
D. Sun, S. Roth, and M. J. Black · 2010
Cited alongside, same era.
Convolutional learning of spatio-temporal features
G. W. Taylor, R. Fergus, Y. LeCun, and C. Bregler · 2010
Cited alongside, same era.
Improving accuracy of optical flow of heegers original method on biomedical images
V. Ulman · 2010
Cited alongside, same era.
Large displacement optical flow: Descriptor matching in variational motion estimation
T. Brox and J. Malik · 2011
Cited alongside, same era.
Large displacement optical flow: Descriptor matching in variational motion estimation
T. Brox and J. Malik · 2011
Cited alongside, same era.
Two-stream convolutional networks for action recognition in videos
K. Simonyan and A. Zisserman · 2014
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Segmentation of dynamic scenes with distributions of spatiotemporally oriented energies
D. Teney and M. Brown · 2014
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C3D: generic features for video analysis
D. Tran, L. D. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 2014
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Rotation-invariant convolutional neural networks for galaxy morphology prediction
S. Dieleman, K. W. Willett, and J. Dambre · 2015
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Flownet: Learning optical flow with convolutional networks
P. Fischer, A. Dosovitskiy, E. Ilg, P. Häusser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox · 2015
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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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K. R. Konda and R. Memisevic · 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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Optical flow modeling and com putation: a survey
D. Fortun, P. Bouthemy, and C. Kervrann · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
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Learning image representations equivariant to ego-motion
D. Jayaraman and K. Grauman · 2015
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Transformation-invariant convolutional jungles
D. Laptev and J. M. Buhmann · 2015
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What can we expect from a V1-MT feedforward architecture for optical flow estimation ?
F. Solari, M. Chessa, N. Medathati, and P. Kornprobst · 2015
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Learning similarity metrics for dynamic scene segmentation
D. Teney, M. Brown, D. Kit, and P. Hall · 2015
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Modeling spatial-temporal clues in a hybrid deep learning framework for video classification
Z. Wu, X. Wang, Y.-G. Jiang, H. Ye, and X. Xue · 2015
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Evaluating two-stream CNN for video classification
H. Ye, Z. Wu, R.-W. Zhao, X. Wang, Y.-G. Jiang, and X. Xue · 2015
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