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Multi-scale inference is commonly used to improve the results of semantic segmentation.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Attention to scale: Scale-aware semantic image segmentation, 2015
Liang-Chieh Chen, Yi Yang, Jiang Wang, Wei Xu, and Alan L. Yuille · 2015
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Parsenet: Looking wider to see better, 2015
Wei Liu, Andrew Rabinovich, and Alexander C. Berg · 2015
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Refinenet: Multi-path refinement networks for high-resolution semantic segmentation, 2016
Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid · 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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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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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 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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Predicting Deeper into the Future of Semantic Segmentation
Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek, and Yann LeCun · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, 2017
Antti Tarvainen and Harri Valpola · 2017
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The mapillary vistas dataset for semantic understanding of street scenes
Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulò, and Peter Kontschieder · 2017
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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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Dual attention network for scene segmentation, 2018
Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu · 2018
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Ocnet: Object context network for scene parsing, 2018
Yuhui Yuan and Jingdong Wang · 2018
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A2̂-nets: Double attention networks
Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, and Jiashi Feng · 2018
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Graph-based global reasoning networks
Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, and Yannis Kalantidis · 2018
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Symbolic graph reasoning meets convolutions
Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, and Eric P Xing · 2018
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Beyond grids: Learning graph representations for visual recognition
Yin Li and Abhinav Gupta · 2018
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Compact generalized non-local network
Kaiyu Yue, Ming Sun, Yuchen Yuan, Feng Zhou, Errui Ding, and Fuxin Xu · 2018
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Ccnet: Criss-cross attention for semantic segmentation
Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation, 2018
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Attention to refine through multi scales for semantic segmentation
Expectation-maximization attention networks for semantic segmentation
Xia Li, Zhisheng Zhong, Jianlong Wu, Yibo Yang, Zhouchen Lin, and Hong Liu · 2019
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Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation, 2019
Bowen Cheng, Maxwell D. Collins, Yukun Zhu, Ting Liu, Thomas S. Huang, Hartwig Adam, and Liang-Chieh Chen · 2019
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Multi-scale self-guided attention for medical image segmentation, 2019
Ashish Sinha and Jose Dolz · 2019
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Ccnet: Criss-cross attention for semantic segmentation
Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu · 2019
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Improving semantic segmentation via video propagation and label relaxation
Yi* Zhu, Karan* Sapra, Fitsum A Reda, Kevin J Shih, Shawn Newsam, Andrew Tao, and Bryan Catanzaro · 2019
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Shiqi Yang and Gang Peng · 2018
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Pyramid attention network for semantic segmentation
Hanchao Li, Pengfei Xiong, Jie An, and Lingxue Wang · 2018
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Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
Yang Zou, Zhiding Yu, B. V. K. Vijaya Kumar, and Jinsong Wang · 2018
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Transductive semi-supervised deep learning using min-max features
Weiwei Shi, Yihong Gong, Chris Ding, Zhiheng MaXiaoyu Tao, and Nanning Zheng · 2018
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Self-training with noisy student improves imagenet classification, 2019
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le · 2019
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Pseudo-labeling and confirmation bias in deep semi-supervised learning
Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 2019
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Zigzagnet: Fusing top-down and bottom-up context for object segmentation
Di Lin, Dingguo Shen, Siting Shen, Yuanfeng Ji, Dani Lischinski, Daniel Cohen-Or, and Hui Huang · 2019
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Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
Qing Lian, Fengmao Lv, Lixin Duan, and Boqing Gong · 2019
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Bidirectional Learning for Domain Adaptation of Semantic Segmentation
Yunsheng Li, Lu Yuan, and Nuno Vasconcelos · 2019
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Confidence Regularized Self-Training
Yang Zou, Zhiding Yu, Xiaofeng Liu, B.V.K. Vijaya Kumar, and Jinsong Wang · 2019
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Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
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Decoupled certainty-driven consistency loss for semi-supervised learning
Yiting Li, Lu Liu, and Robby T Tan · 2019
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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, et al · 2019
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Region mutual information loss for semantic segmentation
Zheng Yang Deng Cai Shuai Zhao, Yang Wang · 2019
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Seamless scene segmentation
Lorenzo Porzi, Samuel Rota Bulo, Aleksander Colovic, and Peter Kontschieder · 2019
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Deeperlab: Single-shot image parser
Tien-Ju Yang, Maxwell D Collins, Yukun Zhu, Jyh-Jing Hwang, Ting Liu, Xiao Zhang, Vivienne Sze, George Papandreou, and Liang-Chieh Chen · 2019
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Segfix: Model-agnostic boundary refinement for segmentation
Yuan Yuhui, Xie Jingyi, Chen Xilin, and Wang Jingdong · 2020
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