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Self-supervised learning (SSL), in particular contrastive learning, has made great progress in recent years.
A visual vocabulary for flower classification
Maria-Elena Nilsback and Andrew Zisserman · 2006
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Matching local self-similarities across images and videos
Eli Shechtman and Michal Irani · 2007
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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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Novel dataset for fine-grained image categorization
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Cats and dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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3D object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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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 visual representations learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A. Efros · 2015
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2015
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Bilinear CNN models for fine-grained visual recognition
Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Adversarial feature learning
Mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
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Rethinking ImageNet pre-training
Kaiming He, Ross Girshick, and Piotr Dollár · 2019
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Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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Once-for-all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Jeff Donahue, Philipp Krahenbuhl, and Trevor Darrell · 2017
Cited alongside, same era.
Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi · 2017
Cited alongside, same era.
Unsupervised representation learning by sorting sequences
Hsin-Ying Lee, Jia-Bin Huang, Maneesh Singh, and Ming-Hsuan Yang · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Cited alongside, same era.
Grad-CAM: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Cited alongside, same era.
Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
Cited alongside, same era.
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2020
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Boostrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altche, Corentin Tallec, Pierre H.Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammand Gheshlaghi Azar, Bial Piot, Koray Kavukcuoglu, Remi Munos, and Michal Valko · 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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Neural random subspace
Yun-Hao Cao, Jianxin Wu, Hanchen Wang, and Joan Lasenby · 2021
Closest in time.
SEED: Self-supervised distillation for visual representation
Zhiyuan Fang, Jianfeng Wang, Lijuan Wang, Lei Zhang, Yezhou Yang, and Zicheng Liu · 2021
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