Fetching the paper…
Reading the bibliography…
By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks.
The hungarian method for the assignment problem
Harold W Kuhn · 1955
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning to see by moving
Pulkit Agrawal, Joao Carreira, and Jitendra Malik · 2015
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Earlier work this paper cites.
Shuffle and learn: unsupervised learning using temporal order verification
Ishan Misra, C Lawrence Zitnick, and Martial Hebert · 2016
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
Earlier work this paper cites.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
Earlier work this paper cites.
Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
Earlier work this paper cites.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Cited alongside, same era.
Colorization as a proxy task for visual understanding
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 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.
Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Richard Zhang, Phillip Isola, and Alexei A Efros · 2017
Cited alongside, same era.
Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 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.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Later among the works it cites.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
Later among the works it cites.
MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark
MMSegmentation Contributors · 2020
Later among the works it cites.
Scene segmentation with dual relation-aware attention network
Jun Fu, Jing Liu, Jie Jiang, Yong Li, Yongjun Bao, and Hanqing Lu · 2020
Later among the works it cites.
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, et al · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Cited alongside, same era.
Improvements to context based self-supervised learning
T Nathan Mundhenk, Daniel Ho, and Barry Y Chen · 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.
Self-labelling via simultaneous clustering and representation learning
Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi · 2019
Cited alongside, same era.
Unsupervised pre-training of image features on non-curated data
Mathilde Caron, Piotr Bojanowski, Julien Mairal, and Armand Joulin · 2019
Cited alongside, same era.
Dual attention network for scene segmentation
Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu · 2019
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Later among the works it cites.
Unsupervised learning of dense visual representations
Pedro O Pinheiro, Amjad Almahairi, Ryan Y Benmaleck, Florian Golemo, and Aaron Courville · 2020
Later among the works it cites.
Efficientdet: Scalable and efficient object detection
Mingxing Tan, Ruoming Pang, and Quoc V Le · 2020
Later among the works it cites.
Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2020
Later among the works it cites.
Part-dependent label noise: Towards instance-dependent label noise
Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, and Masashi Sugiyama · 2020
Later among the works it cites.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
Closest in time.
What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2021
Closest in time.
Dense contrastive learning for self-supervised visual pre-training
Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, and Lei Li · 2021
Closest in time.
Region similarity representation learning
Tete Xiao, Colorado J Reed, Xiaolong Wang, Kurt Keutzer, and Trevor Darrell · 2021
Closest in time.
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
Closest in time.
Selective-supervised contrastive learning with noisy labels
Shikun Li, Xiaobo Xia, Shiming Ge, and Tongliang Liu · 2022
Closest in time.