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Semi-supervised semantic segmentation (SSS) has recently gained increasing research interest as it can reduce the requirement for large-scale fully-annotated training data.
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.
Semantic contours from inverse detectors
Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik · 2011
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Semantic image segmentation with deep convolutional nets and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2014
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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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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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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
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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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Simple does it: Weakly supervised instance and semantic segmentation
Anna Khoreva, Rodrigo Benenson, Jan Hosang, Matthias Hein, and Bernt Schiele · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation
Jiwoon Ahn and Suha Kwak · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Deep co-training for semi-supervised image recognition
Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo Wang, and Alan Yuille · 2018
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Collaborative and adversarial network for unsupervised domain adaptation
Weichen Zhang, Wanli Ouyang, Wen Li, and Dong Xu · 2018
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Semi-supervised semantic segmentation needs strong, high-dimensional perturbations
Geoff French, Timo Aila, Samuli Laine, Michal Mackiewicz, and Graham Finlayson · 2019
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Dual attention network for scene segmentation
Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu · 2019
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Semi-supervised semantic image segmentation with self-correcting networks
Mostafa S Ibrahim, Arash Vahdat, Mani Ranjbar, and William G Macready · 2020
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Guided collaborative training for pixel-wise semi-supervised learning
Zhanghan Ke, Di Qiu, Kaican Li, Qiong Yan, and Rynson WH Lau · 2020
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Gated fully fusion for semantic segmentation
Xiangtai Li, Houlong Zhao, Lei Han, Yunhai Tong, Shaohua Tan, and Kuiyuan Yang · 2020
Cited alongside, same era.
Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al · 2021
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Pixel contrastive-consistent semi-supervised semantic segmentation
Yuanyi Zhong, Bodi Yuan, Hong Wu, Zhiqiang Yuan, Jian Peng, and Yu-Xiong Wang · 2021
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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
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3djcg: A unified framework for joint dense captioning and visual grounding on 3d point clouds
Daigang Cai, Lichen Zhao, Jing Zhang, Lu Sheng, and Dong Xu · 2022
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Exploiting intra-slice and inter-slice redundancy for learning-based lossless volumetric image compression
Zhenghao Chen, Shuhang Gu, Guo Lu, and Dong Xu · 2022
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Semi-supervised segmentation based on error-correcting supervision
Robert Mendel, Luis Antonio de Souza, David Rauber, Joao Paulo Papa, and Christoph Palm · 2020
Cited alongside, same era.
Semi-supervised semantic segmentation with cross-consistency training
Yassine Ouali, Céline Hudelot, and Myriam Tami · 2020
Cited alongside, same era.
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li · 2020
Cited alongside, same era.
Deep high-resolution representation learning for visual recognition
Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al · 2020
Cited alongside, same era.
Pseudoseg: Designing pseudo labels for semantic segmentation
Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, and Tomas Pfister · 2020
Cited alongside, same era.
Semi-supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank
Inigo Alonso, Alberto Sabater, David Ferstl, Luis Montesano, and Ana C Murillo · 2021
Cited alongside, same era.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
Cited alongside, same era.
Lsvc: a learning-based stereo video compression framework
Zhenghao Chen, Guo Lu, Zhihao Hu, Shan Liu, Wei Jiang, and Dong Xu · 2022
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Unbiased subclass regularization for semi-supervised semantic segmentation
Dayan Guan, Jiaxing Huang, Aoran Xiao, and Shijian Lu · 2022
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Segnext: Rethinking convolutional attention design for semantic segmentation
Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou, Zhengning Liu, Ming-Ming Cheng, and Shi-Min Hu · 2022
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Semi-supervised semantic segmentation with error localization network
Donghyeon Kwon and Suha Kwak · 2022
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Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Qiao Yu, and Jifeng Dai · 2022
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Apsnet: Toward adaptive point sampling for efficient 3d action recognition
Jiaheng Liu, Jinyang Guo, and Dong Xu · 2022
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Perturbed and strict mean teachers for semi-supervised semantic segmentation
Yuyuan Liu, Yu Tian, Yuanhong Chen, Fengbei Liu, Vasileios Belagiannis, and Gustavo Carneiro · 2022
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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
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Revisiting weak-to-strong consistency in semi-supervised semantic segmentation
Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang, and Yinghuan Shi · 2022
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St++: Make self-training work better for semi-supervised semantic segmentation
Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, and Yang Gao · 2022
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Segvit: Semantic segmentation with plain vision transformers
Bowen Zhang, Zhi Tian, Quan Tang, Xiangxiang Chu, Xiaolin Wei, Chunhua Shen, and Yifan Liu · 2022
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Instance-specific and model-adaptive supervision for semi-supervised semantic segmentation
Zhen Zhao, Sifan Long, Jimin Pi, Jingdong Wang, and Luping Zhou · 2022
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Augmentation matters: A simple-yet-effective approach to semi-supervised semantic segmentation
Zhen Zhao, Lihe Yang, Sifan Long, Jimin Pi, Luping Zhou, and Jingdong Wang · 2022
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Dc-ssl: Addressing mismatched class distribution in semi-supervised learning
Zhen Zhao, Luping Zhou, Yue Duan, Lei Wang, Lei Qi, and Yinghuan Shi · 2022
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Lassl: label-guided self-training for semi-supervised learning
Zhen Zhao, Luping Zhou, Lei Wang, Yinghuan Shi, and Yang Gao · 2022
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