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Deep neural nets achieve state-of-the-art performance on the problem of optical flow estimation.
Determining optical flow
Berthold K Horn and Brian G Schunck · 1981
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The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields
Michael J Black and P Anandan · 1996
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Secrets of optical flow estimation and their principles
Deqing Sun, Stefan Roth, and Michael J Black · 2010
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A database and evaluation methodology for optical flow
Simon Baker, Daniel Scharstein, JP Lewis, Stefan Roth, Michael J Black, and Richard Szeliski · 2011
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Large displacement optical flow: Descriptor matching in variational motion estimation
T. Brox and J. Malik · 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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Are we ready for autonomous driving? the KITTI vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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A quantitative analysis of current practices in optical flow estimation and the principles behind them
Deqing Sun, Stefan Roth, and Michael J Black · 2014
Earlier work this paper cites.
Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischery, Eddy Ilg, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox, et al · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Cited alongside, same era.
EpicFlow: Edge-preserving interpolation of correspondences for optical flow
Jerome Revaud, Philippe Weinzaepfel, Zaid Harchaoui, and Cordelia Schmid · 2015
Cited alongside, same era.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Cited alongside, same era.
Deconvolution and checkerboard artifacts
Augustus Odena, Vincent Dumoulin, and Chris Olah · 2016
Cited alongside, same era.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
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Slow flow: Exploiting high-speed cameras for accurate and diverse optical flow reference data
Joel Janai, Fatma Guney, Jonas Wulff, Michael J Black, and Andreas Geiger · 2017
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Optical flow estimation using a spatial pyramid network
Anurag Ranjan and Michael J. Black · 2017
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One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Sakurai Kouichi · 2017
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The devil is in the decoder: Classification, regression and gans
Zbigniew Wojna, Jasper R. R. Uijlings, Sergio Guadarrama, Nathan Silberman, Liang-Chieh Chen, Alireza Fathi, and Vittorio Ferrari · 2017
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Unsupervised learning of multi-frame optical flow with occlusions
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Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K Reiter · 2016
Cited alongside, same era.
Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness
Jason J Yu, Adam W Harley, and Konstantinos G Derpanis · 2016
Cited alongside, same era.
Synthesizing robust adversarial examples
Anish Athalye and Ilya Sutskever · 2017
Cited alongside, same era.
Adversarial patch
Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 2017
Cited alongside, same era.
Robust physical-world attacks on deep learning models
Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, and Dawn Song · 2017
Cited alongside, same era.
https://pytorch.org
Cited in the paper.
Joel Janai, Fatma Güney, Anurag Ranjan, Michael Black, and Andreas Geiger · 2018
Later among the works it cites.
Unflow: Unsupervised learning of optical flow with a bidirectional census loss
Simon Meister, Junhwa Hur, and Stefan Roth · 2018
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Learning human optical flow
Anurag Ranjan, Javier Romero, and Michael J Black · 2018
Later among the works it cites.
Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
Later among the works it cites.
Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation
Anurag Ranjan, Varun Jampani, Lukas Balles, Kihwan Kim, Deqing Sun, Jonas Wulff, and Michael J Black · 2019
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