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Recent works in self-supervised learning have demonstrated strong performance on scene-level dense prediction tasks by pretraining with object-centric or region-based correspondence objectives.
Least squares quantization in pcm
Stuart Lloyd · 1982
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Robust analysis of feature spaces: Color image segmentation
Dorin Comaniciu and Peter Meer · 1997
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Mean shift analysis and applications
Dorin Comaniciu and Peter Meer · 1999
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Efficient graph-based image segmentation
Pedro F Felzenszwalb and Daniel P Huttenlocher · 2004
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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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Torchvision the machine-vision package of torch
Sébastien Marcel and Yann Rodriguez · 2010
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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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Slic superpixels compared to state-of-the-art superpixel methods
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk · 2012
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Selective search for object recognition
Jasper RR Uijlings, Koen EA Van De Sande, Theo Gevers, and Arnold WM Smeulders · 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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Image segmentation using k-means clustering algorithm and subtractive clustering algorithm
Nameirakpam Dhanachandra, Khumanthem Manglem, and Yambem Jina Chanu · 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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 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, et al · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Dsets-dbscan: A parameter-free clustering algorithm
Jian Hou, Huijun Gao, and Xuelong Li · 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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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Scaling sgd batch size to 32k for imagenet training
Yang You, Igor Gitman, and Boris Ginsburg · 2017
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Cited alongside, same era.
Unsupervised image segmentation by backpropagation
Asako Kanezaki · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
Emergence of object segmentation in perturbed generative models
Adam Bielski and Paolo Favaro · 2019
Cited alongside, same era.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 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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Picie: Unsupervised semantic segmentation using invariance and equivariance in clustering
Jang Hyun Cho, Utkarsh Mall, Kavita Bala, and Bharath Hariharan · 2021
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Large-scale unsupervised semantic segmentation
Shang-Hua Gao, Zhong-Yu Li, Ming-Hsuan Yang, Ming-Ming Cheng, Junwei Han, and Philip Torr · 2021
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
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Unsupervised object segmentation by redrawing
Mickaël Chen, Thierry Artières, and Ludovic Denoyer · 2019
Cited alongside, same era.
Scaling and benchmarking self-supervised visual representation learning
Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra · 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, Joao F Henriques, and Andrea Vedaldi · 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.
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
Cited alongside, same era.
Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
Cited alongside, same era.
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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Finding an unsupervised image segmenter in each of your deep generative models
Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, and Andrea Vedaldi · 2021
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Selfaugment: Automatic augmentation policies for self-supervised learning
Colorado J Reed, Sean Metzger, Aravind Srinivas, Trevor Darrell, and Kurt Keutzer · 2021
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Casting your model: Learning to localize improves self-supervised representations
Ramprasaath R Selvaraju, Karan Desai, Justin Johnson, and Nikhil Naik · 2021
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Revisiting contrastive methods for unsupervised learning of visual representations
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc V Gool · 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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Object segmentation without labels with large-scale generative models
Andrey Voynov, Stanislav Morozov, and Artem Babenko · 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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Aligning pretraining for detection via object-level contrastive learning
Fangyun Wei, Yue Gao, Zhirong Wu, Han Hu, and Stephen Lin · 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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Detco: Unsupervised contrastive learning for object detection
Enze Xie, Jian Ding, Wenhai Wang, Xiaohang Zhan, Hang Xu, Peize Sun, Zhenguo Li, and Ping Luo · 2021
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Unsupervised object-level representation learning from scene images
Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, and Chen Change Loy · 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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Regioncl: Can simple region swapping contribute to contrastive learning?
Yufei Xu, Qiming Zhang, Jing Zhang, and Dacheng Tao · 2021
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Contrastive object-level pre-training with spatial noise curriculum learning
Chenhongyi Yang, Lichao Huang, and Elliot J Crowley · 2021
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Looking beyond single images for contrastive semantic segmentation learning
Feihu Zhang, Philip Torr, Rene Ranftl, and Stephan Richter · 2021
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, 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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Self-supervised visual representation learning with semantic grouping
Xin Wen, Bingchen Zhao, Anlin Zheng, Xiangyu Zhang, and Xiaojuan Qi · 2022
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