Fetching the paper…
Reading the bibliography…
Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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.
Semantic contours from inverse detectors
Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik · 2011
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
Earlier work this paper cites.
The pascal visual object classes challenge: A retrospective
Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 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.
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
Earlier work this paper cites.
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 · 2017
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
Earlier work this paper cites.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
Earlier work this paper cites.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
Earlier work this paper cites.
Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
Earlier work this paper cites.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
Earlier work this paper cites.
Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
Earlier work this paper cites.
Adversarial learning for semi-supervised semantic segmentation
Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou, Yen-Yu Lin, and Ming-Hsuan Yang · 2018
Earlier work this paper cites.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
Earlier work this paper cites.
Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D Cubuk, and Ian J Goodfellow · 2018
Earlier work this paper cites.
Domain-specific batch normalization for unsupervised domain adaptation
Woong-Gi Chang, Tackgeun You, Seonguk Seo, Suha Kwak, and Bohyung Han · 2019
Cited alongside, same era.
Semi-supervised semantic segmentation with high-and low-level consistency
Sudhanshu Mittal, Maxim Tatarchenko, and Thomas Brox · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Cited alongside, same era.
Pseudo-labeling and confirmation bias in deep semi-supervised learning
Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 2020
Cited alongside, same era.
Pseudo-labeling and confirmation bias in deep semi-supervised learning
Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 2020
Cited alongside, same era.
Semi-supervised semantic segmentation with cross pseudo supervision
Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang · 2021
Later among the works it cites.
Simple copy-paste is a strong data augmentation method for instance segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph · 2021
Later among the works it cites.
Re-distributing biased pseudo labels for semi-supervised semantic segmentation: A baseline investigation
Ruifei He, Jihan Yang, and Xiaojuan Qi · 2021
Later among the works it cites.
Semi-supervised semantic segmentation via adaptive equalization learning
Hanzhe Hu, Fangyun Wei, Han Hu, Qiwei Ye, Jinshi Cui, and Liwei Wang · 2021
Later among the works it cites.
Semi-supervised semantic segmentation with directional context-aware consistency
Xin Lai, Zhuotao Tian, Li Jiang, Shu Liu, Hengshuang Zhao, Liwei Wang, and Jiaya Jia · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
Cited alongside, same era.
Randaugment: Practical automated data augmentation with a reduced search space
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le · 2020
Cited alongside, same era.
Dmt: Dynamic mutual training for semi-supervised learning
Zhengyang Feng, Qianyu Zhou, Qiqi Gu, Xin Tan, Guangliang Cheng, Xuequan Lu, Jianping Shi, and Lizhuang Ma · 2020
Cited alongside, same era.
Semi-supervised semantic segmentation needs strong, high-dimensional perturbations
Geoff French, Timo Aila, Samuli Laine, Michal Mackiewicz, and Graham Finlayson · 2020
Cited alongside, same era.
Semi-supervised semantic image segmentation with self-correcting networks
Mostafa S Ibrahim, Arash Vahdat, Mani Ranjbar, and William G Macready · 2020
Cited alongside, same era.
Guided collaborative training for pixel-wise semi-supervised learning
Zhanghan Ke, Kaican Li Di Qiu, Qiong Yan, and Rynson WH Lau · 2020
Cited alongside, same era.
Self-supervised learning: Generative or contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Zhaoyu Wang, Li Mian, Jing Zhang, and Jie Tang · 2020
Cited alongside, same era.
Trivialaugment: Tuning-free yet state-of-the-art data augmentation
Samuel G. Müller and Frank Hutter · 2021
Later among the works it cites.
A simple baseline for semi-supervised semantic segmentation with strong data augmentation
Jianlong Yuan, Yifan Liu, Chunhua Shen, Zhibin Wang, and Hao Li · 2021
Later among the works it cites.
Pixel contrastive-consistent semi-supervised semantic segmentation
Yuanyi Zhong, Bodi Yuan, Hong Wu, Zhiqiang Yuan, Jian Peng, and Yu-Xiong Wang · 2021
Later among the works it cites.
C3-semiseg: Contrastive semi-supervised segmentation via cross-set learning and dynamic class-balancing
Yanning Zhou, Hang Xu, Wei Zhang, Bin Gao, and Pheng-Ann Heng · 2021
Later among the works it cites.
Pseudoseg: Designing pseudo labels for semantic segmentation
Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, and Tomas Pfister · 2021
Later among the works it cites.
Unbiased subclass regularization for semi-supervised semantic segmentation
Dayan Guan, Jiaxing Huang, Aoran Xiao, and Shijian Lu · 2022
Closest in time.
Semi-supervised npc segmentation with uncertainty and attention guided consistency
Lin Hu, Jiaxin Li, Xingchen Peng, Jianghong Xiao, Bo Zhan, Chen Zu, Xi Wu, Jiliu Zhou, and Yan Wang · 2022
Closest in time.
Semi-supervised semantic segmentation with error localization network
Donghyeon Kwon and Suha Kwak · 2022
Closest in time.
Bootstrapping semantic segmentation with regional contrast
Shikun Liu, Shuaifeng Zhi, Edward Johns, and Andrew J Davison · 2022
Closest in time.
Perturbed and strict mean teachers for semi-supervised semantic segmentation
Yuyuan Liu, Yu Tian, Yuanhong Chen, Fengbei Liu, Vasileios Belagiannis, and Gustavo Carneiro · 2022
Closest in time.
Semi-supervised semantic segmentation using unreliable pseudo-labels
Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, and Xinyi Le · 2022
Closest in time.
St++: Make self-training work better for semi-supervised semantic segmentation
Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, and Yang Gao · 2022
Closest in time.
Lassl: Label-guided self-training for semi-supervised learning
Zhen Zhao, Luping Zhou, Lei Wang, Yinghuan Shi, and Yang Gao · 2022
Closest in time.