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Image segmentation is often ambiguous at the level of individual image patches and requires contextual information to reach label consensus.
A stochastic approximation method
H. Robbins and S. Monro · 1951
Earlier work this paper cites.
Toward automatic phenotyping of developing embryos from videos
F. Ning, D. Delhomme, Yann LeCun, F. Piano, Léon Bottou, and Paolo Emilio Barbano · 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
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Learning hierarchical features for scene labeling
C. Farabet, C. Couprie, L. Najman, and Y. LeCun · 2013
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The role of context for object detection and semantic segmentation in the wild
Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan L. Yuille · 2014
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Recurrent Convolutional Neural Networks for scene labeling
Pedro Pinheiro and Ronan Collobert · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 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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Semantic image segmentation with deep convolutional nets and fully connected CRFs
Chen Liang-Chieh, George Papandreou, Iasonas Kokkinos, kevin murphy, and Alan Yuille · 2015
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Fully Convolutional Networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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U-Net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P.Fischer, and T. Brox · 2015
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Layer normalization
Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 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
Earlier work this paper cites.
Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q. Weinberger · 2016
Earlier work this paper cites.
Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2016
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Gated feedback refinement network for dense image labeling
Md Amirul Islam, Mrigank Rochan, Neil D. B. Bruce, and Yang Wang · 2017
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SegNet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 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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RefineNet: Multi-path refinement networks for high-resolution semantic segmentation
Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid · 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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Fully Convolutional Networks for semantic segmentation
Evan Shelhamer, Jonathan Long, and Trevor Darrell · 2017
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Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges
M. Siam, S. Elkerdawy, M. Jagersand, and S. Yogamani · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz 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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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 · 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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How to start training: The effect of initialization and architecture
Boris Hanin and David Rolnick · 2018
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Virtual-to-real: Learning to control in visual semantic segmentation
Zhang-Wei Hong, Chen Yu-Ming, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Hsuan-Kung Yang, Brian Hsi-Lin Ho, Chih-Chieh Tu, Yueh-Chuan Chang, Tsu-Ching Hsiao, Hsin-Wei Hsiao, Sih-Pin Lai, and Chun-Yi Lee · 2018
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MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark
MMSegmentation Contributors · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le · 2020
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CrossTransformers: spatially-aware few-shot transfer
Carl Doersch, Ankush Gupta, and Andrew Zisserman · 2020
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Scene segmentation with Dual Relation-Aware attention Network
J. Fu, J. Liu, J. Jiang, Y. Li, Y. Bao, and H. Lu · 2020
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Evolution of image segmentation using deep convolutional neural network: A survey
Farhana Sultana, Abu Sufian, and Paramartha Dutta · 2020
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Training Data-Efficient image Transformers and distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
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Non-local neural networks
Xiaolong Wang, Ross B. Girshick, Abhinav Gupta, and Kaiming He · 2018
Cited alongside, same era.
OCNet: Object Context Network for scene parsing
Yuhui Yuan and Jingdong Wang · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
PSANet: Point-wise Spatial Attention Network for scene parsing
Hengshuang Zhao, Yi Zhang, Shu Liu, Jianping Shi, Chen Change Loy, Dahua Lin, and Jiaya Jia · 2018
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 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
Cited alongside, same era.
Adaptive Context Network for scene parsing
Jun Fu, Jing Liu, Yuhang Wang, Yong Li, Yongjun Bao, Jinhui Tang, and Hanqing Lu · 2019
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Max-deeplab: End-to-end panoptic segmentation with mask transformers
Huiyu Wang, Yukun Zhu, Hartwig Adam, Alan L. Yuille, and Liang-Chieh Chen · 2020
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MaX-DeepLab: End-to-end panoptic segmentation with mask transformers
Huiyu Wang, Yukun Zhu, Hartwig Adam, Alan L. Yuille, and Liang-Chieh Chen · 2020
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Axial-Deeplab: Stand-alone axial-attention for panoptic segmentation
Huiyu Wang, Yukun Zhu, Bradley Green, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen · 2020
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Solov2: Dynamic and fast instance segmentation
Xinlong Wang, Rufeng Zhang, Tao Kong, Lei Li, and Chunhua Shen · 2020
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Pytorch image models
Ross Wightman · 2020
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Disentangled Non-local Neural Networks
Minghao Yin, Zhuliang Yao, Yue Cao, Xiu Li, Zheng Zhang, Stephen Lin, and Han Hu · 2020
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Context Prior for scene segmentation
Changqian Yu, Jingbo Wang, Changxin Gao, Gang Yu, Chunhua Shen, and Nong Sang · 2020
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Object-Contextual Representations for semantic segmentation
Yuhui Yuan, Xilin Chen, and Jingdong Wang · 2020
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Resnest: Split-attention networks
Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, and Alexander J. Smola · 2020
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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 H.S. Torr, and Li Zhang · 2020
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ViViT: A video vision transformer
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid · 2021
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Is space-time attention all you need for video understanding?
Gedas Bertasius, Heng Wang, and Lorenzo Torresani · 2021
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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, Jakob Uszkoreit, and Neil Houlsby · 2021
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Vision transformer models
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 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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Image segmentation using deep learning: A survey
Shervin Minaee, Yuri Boykov, F. Porikli, Antonio J. Plaza, N. Kehtarnavaz, and Demetri Terzopoulos · 2021
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How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer · 2021
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