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Self-supervised learning of visual representations has been focusing on learning content features, which do not capture object motion or location, and focus on identifying and differentiating objects in images and videos.
Determining optical flow
Berthold K. P. Horn and Brian G. Schunck · 1981
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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High accuracy optical flow estimation based on a theory for warping
Thomas Brox, Andres Bruhn, Nils Papenberg, and Joachim ´ Weickert · 2004
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, John Winn Christopher K. I. Williams, and Andrew Zisserman · 2010
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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 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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Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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ibot: Image bert pre-training with online tokenizer
Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong · 2012
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Vision meets robotics: The kitti dataset
Geiger A., Lenz P., Stiller C., and Urtasun R · 2013
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Object scene flow for autonomous vehicles
Moritz Menze and Andreas Geiger · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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The hci benchmark suite: Stereo and flow ground truth with uncertainties for urban autonomous driving
Kondermann D., Nair R., Honauer K., Krispin K., Andrulis J., Brock A., Gussefeld B., Rahimimoghaddam M., Hofmann S., and Brenner C. et al · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Mayer N., Ilg E., Hausser P., Fischer P., Cremers D., Dosovitskiy A., and Brox T · 2016
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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
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Multi-task self-supervised visual learning
Carl Doersch and Andrew Zisserman · 2017
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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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The 2017 davis challenge on video object segmentation
Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbelaez, Alexander Sorkine-Hornung, and Luc Van Gool · 2017
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Optical flow estimation using a spatial pyramid network
Anurag Ranjan and Michael J. Black · 2017
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Unsupervised deep learning for optical flow estimation
Zhe Ren, Junchi Yan, Bingbing Ni, Bin Liu, Xiaokang Yang, and Hongyuan Zha · 2017
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Deep clustering for unsupervised learning
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Liteflownet: A lightweight convolutional neural network for optical flow estimation
Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy · 2018
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Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Adam Trischler, and Yoshua Bengio · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Volumetric correspondence networks for optical flow
Gengshan Yang and Deva Ramanan · 2019
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Unsupervised deep epipolar flow for stationary or dynamic scenes
Yiran Zhong, Pan Ji, Jianyuan Wang, Yuchao Dai, and Hongdong Li · 2019
Cited alongside, same era.
Semantic understanding of scenes through the ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2019
Cited alongside, same era.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Cited alongside, same era.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2020
Cited alongside, same era.
Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
Cited alongside, same era.
Dense contrastive learning for self-supervised visual pre-training
Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, and Lei Li · 2021
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Region similarity representation learning
Tete Xiao, Colorado J Reed, Xiaolong Wang, Kurt Keutzer, and Trevor Darrell · 2021
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Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning
Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, and Han Hu · 2021
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Rethinking self-supervised correspondence learning: A video frame-level similarity perspective
Jiarui Xu and Xiaolong Wang · 2021
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Instance localization for self-supervised detection pretraining
Ceyuan Yang, Zhirong Wu, Bolei Zhou, and Stephen Lin · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Unsupervised learning of optical flow with deep feature similarity
Woobin Im, Tae-Kyun Kim, and Sung-Eui Yoon · 2020
Cited alongside, same era.
Space-time correspondence as a contrastive random walk
Allan A. Jabri, Andrew Owens, and Alexei A. Efros · 2020
Cited alongside, same era.
What matters in unsupervised optical flow
Rico Jonschkowski, Austin Stone, Jonathan T. Barron, Ariel Gordon, Kurt Konolige, and Anelia Angelova · 2020
Cited alongside, same era.
Learning by analogy: Reliable supervision from transformations for unsupervised optical flow estimation
Liang Liu, Jiangning Zhang, Ruifei He, Yong Liu, Yabiao Wang, Ying Tai, Donghao Luo, Chengjie Wang, Jilin Li, and Feiyue Huang · 2020
Cited alongside, same era.
Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
Cited alongside, same era.
Maskflownet: Asymmetric feature matching with learnable occlusion mask
Shengyu Zhao, Yilun Sheng, Yue Dong, Eric I-Chao, and Chang Yan Xu · 2020
Cited alongside, same era.
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
Later among the works it cites.
Co-training transformer with videos and images improves action recognition
Bowen Zhang, Jiahui Yu, Christopher Fifty, and Wei Han · 2021
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Masked siamese networks for label-efficient learning
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, and Nicolas Ballas · 2022
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Deep equilibrium optical flow estimation
Shaojie Bai, Zhengyang Geng, Yash Savani, and J. Zico Kolter · 2022
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei · 2022
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Learning pixel trajectories with multiscale contrastive random walks
Zhangxing Bian, Allan Jabri, Alexei A. Efros, and Andrew Owens · 2022
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Intra-instance vicreg: Bag of self-supervised image patch embedding
Yubei Chen, Adrien Bardes, Zengyi Li, and Yann LeCun · 2022
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Omnimae: Single model masked pretraining on images and videos
Rohit Girdhar, Alaaeldin El-Nouby, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll, and Ross Girshick · 2022
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Object discovery and representation networks
Olivier J. Hénaff, Skanda Koppula, Evan Shelhamer, Daniel Zoran, Andrew Jaegle, Andrew Zisserman, João Carreira, and Relja Arandjelović · 2022
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A path towards autonomous machine intelligence
Yann LeCun · 2022
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Efficient self-supervised vision transformers for representation learning
Chunyuan Li, Jianwei Yang, Pengchuan Zhang, Mei Gao, Bin Xiao, Xiyang Dai, Lu Yuan, and Jianfeng Gao · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Self-supervised video pretraining yeilds strong image representations
Nikhil Parthasarathy, M. Ali Eslami, João Carreira, and Olivier J. Hénaff · 2022
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Object permanence emerges in a random walk along memory
Pavel Tokmakov, Allan Jabri, Jie Li, and Adrien Gaidon · 2022
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Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, and Jovana Mitrovic · 2022
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Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang · 2022
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Cp2: Copy-paste contrastive pretraining for semantic segmentation
Feng Wang, Huiyu Wang, Chen Wei, Alan Yuille, and Wei Shen · 2022
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Inscon: Instance consistency feature representation via self-supervised learning
Junwei Yang, Ke Zhang, Zhaolin Cui, Jinming Su, Junfeng Luo, and Xiaolin Wei · 2022
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Self-supervised learning from images with a joint-embedding predictive architecture
Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas · 2023
Closest in time.
Location-aware self-supervised transformers
Mathilde Caron, Neil Houlsby, and Cordelia Schmid · 2023
Closest in time.