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In this paper, we study the problem of class imbalance in semantic segmentation.
SMOTE: Synthetic minority over-sampling technique
Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer · 2002
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The class imbalance problem: A systematic study
Nathalie Japkowicz and Shaju Stephen · 2002
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Pearson correlation coefficient
Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen · 2009
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Learning from imbalanced data
Haibo He and Edwardo A Garcia · 2009
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ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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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 · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning deep representation for imbalanced classification
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang · 2016
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Learning deep representation for imbalanced classification
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi · 2016
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Relay backpropagation for effective learning of deep convolutional neural networks
Li Shen, Zhouchen Lin, and Qingming Huang · 2016
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2016
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Towards good practices for recognition & detection
Q Zhong, C Li, Y Zhang, H Sun, S Yang, D Xie, and S Pu · 2016
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Gated feedback refinement network for dense image labeling
Md Amirul Islam, Mrigank Rochan, Neil DB Bruce, and Yang Wang · 2017
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
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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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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross B. Girshick, Kaiming He, Bharath Hariharan, and Serge J. Belongie · 2017
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Full-resolution residual networks for semantic segmentation in street scenes
Tobias Pohlen, Alexander Hermans, Markus Mathias, and Bastian Leibe · 2017
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Learning to model the tail
Yu-Xiong Wang, Deva Ramanan, and Martial Hebert · 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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Large-scale long-tailed recognition in an open world
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X Yu · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng · 2019
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Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition
Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen · 2019
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MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark
MMSegmentation Contributors · 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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Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
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A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, and Maciej A Mazurowski · 2018
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A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, and Maciej A. Mazurowski · 2018
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Coco-stuff: Thing and stuff classes in context
Holger Caesar, Jasper Uijlings, and Vittorio Ferrari · 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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Path aggregation network for instance segmentation
Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia · 2018
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Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective
Muhammad Abdullah Jamal, Matthew Brown, Ming-Hsuan Yang, Liqiang Wang, and Boqing Gong · 2020
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Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2020
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Overcoming classifier imbalance for long-tail object detection with balanced group softmax
Yu Li, Tao Wang, Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, and Jiashi Feng · 2020
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Balanced meta-softmax for long-tailed visual recognition
Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, and Hongsheng Li · 2020
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Equalization loss for long-tailed object recognition
Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, and Junjie Yan · 2020
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Long-tailed classification by keeping the good and removing the bad momentum causal effect
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang · 2020
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Long-tailed classification by keeping the good and removing the bad momentum causal effect
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang · 2020
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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
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Object-contextual representations for semantic segmentation
Yuhui Yuan, Xilin Chen, and Jingdong Wang · 2020
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, and Furu Wei · 2021
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Reslt: Residual learning for long-tailed recognition
Jiequan Cui, Shu Liu, Zhuotao Tian, Zhisheng Zhong, and Jiaya Jia · 2021
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Parametric contrastive learning
Jiequan Cui, Zhisheng Zhong, Shu Liu, Bei Yu, and Jiaya Jia · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Seesaw loss for long-tailed instance segmentation
Jiaqi Wang, Wenwei Zhang, Yuhang Zang, Yuhang Cao, Jiangmiao Pang, Tao Gong, Kai Chen, Ziwei Liu, Chen Change Loy, and Dahua Lin · 2021
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Long-tailed recognition by routing diverse distribution-aware experts
Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella X. Yu · 2021
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Improving calibration for long-tailed recognition
Zhisheng Zhong, Jiequan Cui, Shu Liu, and Jiaya Jia · 2021
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