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Progress in self-supervised learning has brought strong general image representation learning methods.
The hungarian method for the assignment problem
Harold W Kuhn · 1955
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Objects in context
Andrew Rabinovich, Andrea Vedaldi, Carolina Galleguillos, Eric Wiewiora, and Serge Belongie · 2007
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Maps of information flow reveal community structure in complex networks
Martin Rosvall and Carl T Bergstrom · 2007
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Object categorization using co-occurrence, location and appearance
Carolina Galleguillos, Andrew Rabinovich, and Serge Belongie · 2008
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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What is a good evaluation measure for semantic segmentation?
Gabriela Csurka, Diane Larlus, Florent Perronnin, and France Meylan · 2013
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Unsupervised object discovery and localization in images and videos
Minsu Cho, Suha Kwak, Ivan Laptev, Cordelia Schmid, and Jean Ponce · 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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Discriminative unsupervised feature learning with exemplar convolutional neural networks, 2015
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2015
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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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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Context encoders: Feature learning by inpainting, 2016
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Unsupervised learning by predicting noise, 2017
Piotr Bojanowski and Armand Joulin · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2017
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Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
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Coco-stuff: Thing and stuff classes in context
Holger Caesar, Jasper Uijlings, and Vittorio Ferrari · 2018
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Cited alongside, same era.
Rethinking imagenet pre-training, 2018
Kaiming He, Ross Girshick, and Piotr Dollár · 2018
Cited alongside, same era.
Self-supervised feature learning by learning to spot artifacts, 2018
Simon Jenni and Paolo Favaro · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin · 2018
Cited alongside, same era.
Pytorch lightning
et al. Falcon, WA · 2019
Cited alongside, same era.
Object relational graph with teacher-recommended learning for video captioning
Ziqi Zhang, Yaya Shi, Chunfeng Yuan, Bing Li, Peijin Wang, Weiming Hu, and Zheng-Jun Zha · 2020
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
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Unsupervised part discovery from contrastive reconstruction
Subhabrata Choudhury, Iro Laina, Christian Rupprecht, and Andrea Vedaldi · 2021
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Twins: Revisiting spatial attention design in vision transformers
Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, and Chunhua Shen · 2021
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Scops: Self-supervised co-part segmentation
Wei-Chih Hung, Varun Jampani, Sifei Liu, Pavlo Molchanov, Ming-Hsuan Yang, and Jan Kautz · 2019
Cited alongside, same era.
Segsort: Segmentation by discriminative sorting of segments
Jyh-Jing Hwang, Stella X Yu, Jianbo Shi, Maxwell D Collins, Tien-Ju Yang, Xiao Zhang, and Liang-Chieh Chen · 2019
Cited alongside, same era.
Invariant information clustering for unsupervised image classification and segmentation
Xu Ji, João F. Henriques, and Andrea Vedaldi · 2019
Cited alongside, same era.
Panoptic segmentation
Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, and Piotr Dollár · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Aet vs. aed: Unsupervised representation learning by auto-encoding transformations rather than data, 2019
Liheng Zhang, Guo-Jun Qi, Liqiang Wang, and Jiebo Luo · 2019
Cited alongside, same era.
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An image is worth 16x16 words: Transformers for image recognition at scale, 2021
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Masked autoencoders are scalable vision learners
He et al · 2021
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An empirical study of training self-supervised vision transformers
Xinlei Chen et al · 2021
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Large-scale unsupervised semantic segmentation, 2021
Shang-Hua Gao, Zhong-Yu Li, Ming-Hsuan Yang, Ming-Ming Cheng, Junwei Han, and Philip Torr · 2021
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Obow: Online bag-of-visual-words generation for self-supervised learning
Spyros Gidaris, Andrei Bursuc, Gilles Puy, Nikos Komodakis, Matthieu Cord, and Patrick Perez · 2021
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Efficient visual pretraining with contrastive detection
Olivier J Hénaff, Skanda Koppula, Jean-Baptiste Alayrac, Aaron van den Oord, Oriol Vinyals, and João Carreira · 2021
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Dense semantic contrast for self-supervised visual representation learning
Xiaoni Li, Yu Zhou, Yifei Zhang, Aoting Zhang, Wei Wang, Ning Jiang, Haiying Wu, and Weiping Wang · 2021
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Self-emd: Self-supervised object detection without imagenet, 2021
Songtao Liu, Zeming Li, and Jian Sun · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Imagenet-21k pretraining for the masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor · 2021
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Spatially consistent representation learning
Byungseok Roh, Wuhyun Shin, Ildoo Kim, and Sungwoong Kim · 2021
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Localizing objects with self-supervised transformers and no labels
Oriane Siméoni, Gilles Puy, Huy V Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet, and Jean Ponce · 2021
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Unsupervised semantic segmentation by contrasting object mask proposals
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc Van Gool · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2021
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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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Cvt: Introducing convolutions to vision transformers
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang · 2021
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