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Establishing correspondence between images or scenes is a significant challenge in computer vision, especially given occlusions, viewpoint changes, and varying object appearances.
The perception of the visual world
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Self-organizing neural network that discovers surfaces in random-dot stereograms
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Signature verification using a" siamese" time delay neural network
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Object perception, object-directed action, and physical knowledge in infancy."
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Slow feature analysis: Unsupervised learning of invariances
Laurenz Wiskott and Terrence J Sejnowski · 2002
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Multiple view geometry in computer vision
Richard Hartley and Andrew Zisserman · 2003
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What went where
Josh Wills, Sameer Agarwal, and Serge J. Belongie · 2003
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Kernel-based object tracking
Dorin Comaniciu, Visvanathan Ramesh, and Peter Meer · 2003
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High accuracy optical flow estimation based on a theory for warping
Thomas Brox, Andrés Bruhn, Nils Papenberg, and Joachim Weickert · 2004
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Efficient mean-shift tracking via a new similarity measure
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Dimensionality reduction by learning an invariant mapping
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Depth estimation using monocular and stereo cues
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People-tracking-by-detection and people-detection-by-tracking
Mykhaylo Andriluka, Stefan Roth, and Bernt Schiele · 2008
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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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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Sift flow: Dense correspondence across scenes and its applications
Ce Liu, Jenny Yuen, and Antonio Torralba · 2010
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Zdenek Kalal, Krystian Mikolajczyk, and Jiri Matas · 2011
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A naturalistic open source movie for optical flow evaluation
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Towards understanding action recognition
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Deepface: Closing the gap to human-level performance in face verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf · 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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Discrete optimization for optical flow
Moritz Menze, Christian Heipke, and Andreas Geiger · 2015
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
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Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2015
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Learning to see by moving
Pulkit Agrawal, Joao Carreira, and Jitendra Malik · 2015
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Unsupervised learning of visual representations using videos
Xiaolong Wang and Abhinav Gupta · 2015
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Unsupervised learning of spatiotemporally coherent metrics
Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, and Yann LeCun · 2015
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, Ruslan Salakhutdinov, et al · 2015
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A benchmark dataset and evaluation methodology for video object segmentation
Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus Gross, and Alexander Sorkine-Hornung · 2016
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Fully-convolutional siamese networks for object tracking
Luca Bertinetto, Jack Valmadre, Joao F Henriques, Andrea Vedaldi, and Philip HS Torr · 2016
Joint-task self-supervised learning for temporal correspondence
Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Jan Kautz, and Ming-Hsuan Yang · 2019
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Tracking without bells and whistles
Philipp Bergmann, Tim Meinhardt, and Laura Leal-Taixe · 2019
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Self-supervised spatiotemporal learning via video clip order prediction
Dejing Xu, Jun Xiao, Zhou Zhao, Jian Shao, Di Xie, and Yueting Zhuang · 2019
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Learning video representations using contrastive bidirectional transformer
Chen Sun, Fabien Baradel, Kevin Murphy, and Cordelia Schmid · 2019
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Video representation learning by dense predictive coding
Tengda Han, Weidi Xie, and Andrew Zisserman · 2019
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Language models are unsupervised multitask learners
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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An uncertain future: Forecasting from static images using variational autoencoders
Jacob Walker, Carl Doersch, Abhinav Gupta, and Martial Hebert · 2016
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Anticipating visual representations from unlabeled video
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Deep predictive coding networks for video prediction and unsupervised learning
William Lotter, Gabriel Kreiman, and David Cox · 2016
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Shuffle and learn: unsupervised learning using temporal order verification
Ishan Misra, C Lawrence Zitnick, and Martial Hebert · 2016
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Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Raft: Recurrent all-pairs field transforms for optical flow
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Space-time correspondence as a contrastive random walk
Allan Jabri, Andrew Owens, and Alexei Efros · 2020
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What should not be contrastive in contrastive learning
Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Augment your batch: Improving generalization through instance repetition
Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, and Daniel Soudry · 2020
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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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Emerging properties in self-supervised vision transformers
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A large-scale study on unsupervised spatiotemporal representation learning
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Broaden your views for self-supervised video learning
Adria Recasens, Pauline Luc, Jean-Baptiste Alayrac, Luyu Wang, Florian Strub, Corentin Tallec, Mateusz Malinowski, Viorica Pătrăucean, Florent Altché, Michal Valko, et al · 2021
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Spatiotemporal contrastive video representation learning
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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 · 2021
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Ego4d: Around the world in 3,000 hours of egocentric video
Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, et al · 2021
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Unifying flow, stereo and depth estimation
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Masked autoencoders are scalable vision learners
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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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Simmim: A simple framework for masked image modeling
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Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
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Masked siamese networks for label-efficient learning
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https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
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