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Optical flow estimation is a classical yet challenging task in computer vision.
Determining optical flow
Berthold KP Horn and Brian G Schunck · 1981
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A framework for the robust estimation of optical flow
Michael J Black and Padmanabhan Anandan · 1993
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Lucas/kanade meets horn/schunck: Combining local and global optic flow methods
Andrés Bruhn, Joachim Weickert, and Christoph Schnörr · 2005
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A naturalistic open source movie for optical flow evaluation
Daniel J Butler, Jonas Wulff, Garrett B Stanley, and Michael J Black · 2012
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 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
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Joint 3d estimation of vehicles and scene flow
Moritz Menze, Christian Heipke, and Andreas Geiger · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Full flow: Optical flow estimation by global optimization over regular grids
Qifeng Chen and Vladlen Koltun · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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The hci benchmark suite: Stereo and flow ground truth with uncertainties for urban autonomous driving
Daniel Kondermann, Rahul Nair, Katrin Honauer, Karsten Krispin, Jonas Andrulis, Alexander Brock, Burkhard Gussefeld, Mohsen Rahimimoghaddam, Sabine Hofmann, Claus Brenner, et al · 2016
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Understanding the effective receptive field in deep convolutional neural networks
Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Residual networks behave like ensembles of relatively shallow networks
Andreas Veit, Michael J Wilber, and Serge Belongie · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Large kernel matters–improve semantic segmentation by global convolutional network
Chao Peng, Xiangyu Zhang, Gang Yu, Guiming Luo, and Jian Sun · 2017
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
Cited alongside, same era.
Liteflownet: A lightweight convolutional neural network for optical flow estimation
Hierarchical discrete distribution decomposition for match density estimation
Zhichao Yin, Trevor Darrell, and Fisher Yu · 2019
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Batch normalization biases residual blocks towards the identity function in deep networks
Soham De and Sam Smith · 2020
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A lightweight optical flow cnn—revisiting data fidelity and regularization
Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy · 2020
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Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
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Displacement-invariant matching cost learning for accurate optical flow estimation
Jianyuan Wang, Yiran Zhong, Yuchao Dai, Kaihao Zhang, Pan Ji, and Hongdong Li · 2020
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Maskflownet: Asymmetric feature matching with learnable occlusion mask
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Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy · 2018
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
Cited alongside, same era.
Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
Cited alongside, same era.
Understanding convolution for semantic segmentation
Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell · 2018
Cited alongside, same era.
Local relation networks for image recognition
Han Hu, Zheng Zhang, Zhenda Xie, and Stephen Lin · 2019
Cited alongside, same era.
Iterative residual refinement for joint optical flow and occlusion estimation
Junhwa Hur and Stefan Roth · 2019
Cited alongside, same era.
Super-convergence: Very fast training of neural networks using large learning rates
Leslie N Smith and Nicholay Topin · 2019
Cited alongside, same era.
Shengyu Zhao, Yilun Sheng, Yue Dong, Eric I Chang, Yan Xu, et al · 2020
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Learning to estimate hidden motions with global motion aggregation
Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, and Richard Hartley · 2021
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Learning optical flow from a few matches
Shihao Jiang, Yao Lu, Hongdong Li, and Richard Hartley · 2021
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Flexconv: Continuous kernel convolutions with differentiable kernel sizes
David W Romero, Robert-Jan Bruintjes, Jakub M Tomczak, Erik J Bekkers, Mark Hoogendoorn, and Jan C van Gemert · 2021
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Ckconv: Continuous kernel convolution for sequential data
David W Romero, Anna Kuzina, Erik J Bekkers, Jakub M Tomczak, and Mark Hoogendoorn · 2021
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High-resolution optical flow from 1d attention and correlation
Haofei Xu, Jiaolong Yang, Jianfei Cai, Juyong Zhang, and Xin Tong · 2021
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Separable flow: Learning motion cost volumes for optical flow estimation
Feihu Zhang, Oliver J Woodford, Victor Adrian Prisacariu, and Philip HS Torr · 2021
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Scaling up your kernels to 31x31: Revisiting large kernel design in cnns
Xiaohan Ding, Xiangyu Zhang, Yizhuang Zhou, Jungong Han, Guiguang Ding, and Jian Sun · 2022
Closest in time.
Learning optical flow with adaptive graph reasoning
Ao Luo, Fan Yang, Kunming Luo, Xin Li, Haoqiang Fan, and Shuaicheng Liu · 2022
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