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The state of the art in semantic segmentation is steadily increasing in performance, resulting in more precise and reliable segmentations in many different applications.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Semantic contours from inverse detectors
Bharath Hariharan, Pablo Arbelaez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik · 2011
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
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Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
Earlier work this paper cites.
Semantic image segmentation with deep convolutional nets and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 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
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Cut, paste and learn: Surprisingly easy synthesis for instance detection
Debidatta Dwibedi, Ishan Misra, and Martial Hebert · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade
Xiaoxiao Li, Ziwei Liu, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille · 2018
Cited alongside, same era.
Modeling visual context is key to augmenting object detection datasets
Nikita Dvornik, Julien Mairal, and Cordelia Schmid · 2018
Cited alongside, same era.
Self-ensembling for visual domain adaptation
Geoffrey French, Michal Mackiewicz, and Mark H. Fisher · 2018
Cited alongside, same era.
Adversarial learning for semi-supervised semantic segmentation
Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou, Yen-Yu Lin, and Ming-Hsuan Yang · 2018
Cited alongside, same era.
Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model
Semi-supervised semantic segmentation with high-and low-level consistency
Sudhanshu Mittal, Maxim Tatarchenko, and Thomas Brox · 2019
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Ke-gan: Knowledge embedded generative adversarial networks for semi-supervised scene parsing
Mengshi Qi, Yunhong Wang, Jie Qin, and Annan Li · 2019
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Learning to generate synthetic data via compositing
Shashank Tripathi, Siddhartha Chandra, Amit Agrawal, Ambrish Tyagi, James M. Rehg, and Visesh Chari · 2019
Later among the works it cites.
Interpolation consistency training for semi-supervised learning
Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, and David Lopez-Paz · 2019
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le · 2019
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Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, and Pheng-Ann Heng · 2018
Cited alongside, same era.
Deep semi-supervised segmentation with weight-averaged consistency targets
Christian S Perone and Julien Cohen-Adad · 2018
Cited alongside, same era.
Learning to segment via cut-and-paste
Tal Remez, Jonathan Huang, and Matthew Brown · 2018
Cited alongside, same era.
Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring
David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 2019
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
Cited alongside, same era.
Instaboost: Boosting instance segmentation via probability map guided copy-pasting
Hao-Shu Fang, Jianhua Sun, Runzhong Wang, Minghao Gou, Yong-Lu Li, and Cewu Lu · 2019
Cited alongside, same era.
Semi-supervised semantic segmentation needs strong, varied perturbations
Geoff French, Samuli Laine, Timo Aila, and Michal Mackiewicz · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Sanghyuk Chun, Seong Joon Oh, Youngjoon Yoo, and Junsuk Choe · 2019
Later among the works it cites.
Semi-supervised learning in video sequences for urban scene segmentation
Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D Collins, Ekin D Cubuk, Barret Zoph, Hartwig Adam, and Jonathon Shlens · 2020
Closest in time.
Semi-supervised semantic segmentation via dynamic self-training and class-balanced curriculum
Zhengyang Feng, Qianyu Zhou, Guangliang Cheng, Xin Tan, Jianping Shi, and Lizhuang Ma · 2020
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Milking cowmask for semi-supervised image classification
Geoff French, Avital Oliver, and Tim Salimans · 2020
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Understanding and enhancing mixed sample data augmentation
Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, Adam Prügel-Bennett, and Jonathon Hare · 2020
Closest in time.
Structured consistency loss for semi-supervised semantic segmentation
Jongmok Kim, Jooyoung Jang, and Hyunwoo Park · 2020
Closest in time.
Semi-supervised semantic segmentation with cross-consistency training
Yassine Ouali, Céline Hudelot, and Myriam Tami · 2020
Closest in time.
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
Closest in time.