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Current semantic segmentation methods focus only on mining "local" context, i.e., dependencies between pixels within individual images, by context-aggregation modules (e.g., dilated convolution, neural attention) or structure-aware optimization criteria (e.g., IoU-like loss).
Image quality assessment: from error visibility to structural similarity
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Dimensionality reduction by learning an invariant mapping
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Semantic object classes in video: A high-definition ground truth database
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Maxime Bucher, Stéphane Herbin, and Frédéric Jurie · 2016
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Liang-Chieh Chen, Yi Yang, Jiang Wang, Wei Xu, and Alan L Yuille · 2016
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Large-margin softmax loss for convolutional neural networks
Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Multi-scale context aggregation by dilated convolutions
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Deep watershed transform for instance segmentation
Min Bai and Raquel Urtasun · 2017
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Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Semantic instance segmentation with a discriminative loss function
Bert De Brabandere, Davy Neven, and Luc Van Gool · 2017
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Semantic instance segmentation via deep metric learning
Alireza Fathi, Zbigniew Wojna, Vivek Rathod, Peng Wang, Hyun Oh Song, Sergio Guadarrama, and Kevin P Murphy · 2017
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Segmentation-aware convolutional networks using local attention masks
Adam W Harley, Konstantinos G Derpanis, and Iasonas Kokkinos · 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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Deep learning markov random field for semantic segmentation
Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, and Xiaoou Tang · 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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The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko · 2018
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Coco-stuff: Thing and stuff classes in context
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Li Zhang, Xiangtai Li, Anurag Arnab, Kuiyuan Yang, Yunhai Tong, and Philip HS Torr · 2019
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Region mutual information loss for semantic segmentation
Shuai Zhao, Yang Wang, Zheng Yang, and Deng Cai · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Contrastive learning of global and local features for medical image segmentation with limited annotations
Krishna Chaitanya, Ertunc Erdil, Neerav Karani, and Ender Konukoglu · 2020
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Holger Caesar, Jasper Uijlings, and Vittorio Ferrari · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Adaptive affinity fields for semantic segmentation
Tsung-Wei Ke, Jyh-Jing Hwang, Ziwei Liu, and Stella X Yu · 2018
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Recurrent pixel embedding for instance grouping
Shu Kong and Charless Fowlkes · 2018
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Self-supervised learning of pretext-invariant representations
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Cross-batch memory for embedding learning
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Can semantic labels assist self-supervised visual representation learning?
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On mutual information in contrastive learning for visual representations
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Delving into inter-image invariance for unsupervised visual representations
Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, and Chen Change Loy · 2020
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Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, and Han Hu · 2020
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Disentangled non-local neural networks
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Context prior for scene segmentation
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Object-contextual representations for semantic segmentation
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Contrastive learning with hard negative samples
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