Fetching the paper…
Reading the bibliography…
Collecting labeled data for the task of semantic segmentation is expensive and time-consuming, as it requires dense pixel-level annotations.
Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
Earlier work this paper cites.
Distance metric learning for large margin nearest neighbor classification
Kilian Q. Weinberger and Lawrence K. Saul · 2009
Earlier work this paper cites.
Learning hierarchical features for scene labeling
Clément Farabet, Camille Couprie, Laurent Najman, and Yann LeCun · 2013
Earlier work this paper cites.
Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
Earlier work this paper cites.
Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
Jifeng Dai, Kaiming He, and Jian Sun · 2015
Earlier work this paper cites.
The pascal visual object classes challenge: A retrospective
Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and Andrew Zisserman · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
Earlier work this paper cites.
Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation
George Papandreou, Liang-Chieh Chen, Kevin P. Murphy, and Alan L. Yuille · 2015
Earlier work this paper cites.
Large margin deep neural networks: Theory and algorithms
Shizhao Sun, Wei Chen, Liwei Wang, and Tie-Yan Liu · 2015
Earlier work this paper cites.
What’s the point: Semantic segmentation with point supervision
Amy Bearman, Olga Russakovsky, Vittorio Ferrari, and Li Fei-Fei · 2016
Earlier work this paper cites.
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.
Scribblesup: Scribble-supervised convolutional networks for semantic segmentation
Di Lin, Jifeng Dai, Jiaya Jia, Kaiming He, and Jian Sun · 2016
Earlier work this paper cites.
Large-margin softmax loss for convolutional neural networks
Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang · 2016
Earlier work this paper cites.
Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
Earlier work this paper cites.
Gaussian conditional random field network for semantic segmentation
Raviteja Vemulapalli, Oncel Tuzel, Ming-Yu Liu, and Rama Chellappa · 2016
Earlier work this paper cites.
Semantic instance segmentation with a discriminative loss function
Bert De Brabandere, Davy Neven, and Luc Van Gool · 2017
Earlier work this paper cites.
Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Segmentation-aware convolutional networks using local attention masks
Adam W. Harley, Konstantinos G. Derpanis, and Iasonas Kokkinos · 2017
Earlier work this paper cites.
Simple does it: Weakly supervised instance and semantic segmentation
Anna Khoreva, Rodrigo Benenson, Jan Hosang, Matthias Hein, and Bernt Schiele · 2017
Earlier work this paper cites.
Semi supervised semantic segmentation using generative adversarial network
Nasim Souly, Concetto Spampinato, and Mubarak Shah · 2017
Earlier work this paper cites.
Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
Cited alongside, same era.
Large margin deep networks for classification
Gamaleldin F. Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio · 2018
Cited alongside, same era.
Weakly-supervised semantic segmentation network with deep seeded region growing
Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu, and Jingdong Wang · 2018
Cited alongside, same era.
Adversarial learning for semi-supervised semantic segmentation
Region mutual information loss for semantic segmentation
Shuai Zhao, Yang Wang, Zheng Yang, and Deng Cai · 2019
Later among the works it cites.
Single-stage semantic segmentation from image labels
Nikita Araslanov and Stefan Roth · 2020
Closest in time.
Contrastive learning of global and local features for medical image segmentation with limited annotations
Krishna Chaitanya, Ertunc Erdil, Neerav Karani, and Ender Konukoglu · 2020
Closest in time.
Naive-student: Leveraging semi-supervised learning in video sequences for urban scene segmentation
Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, and Jonathon Shlens · 2020
Closest in time.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou, Yen-Yu Lin, and Ming-Hsuan Yang · 2018
Cited alongside, same era.
Adaptive affinity fields for semantic segmentation
Tsung-Wei Ke, Jyh-Jing Hwang, Ziwei Liu, and Stella X Yu · 2018
Cited alongside, same era.
Recurrent pixel embedding for instance grouping
Shu Kong and Charless C. Fowlkes · 2018
Cited alongside, same era.
Normalized cut loss for weakly-supervised CNN segmentation
Meng Tang, Abdelaziz Djelouah, Federico Perazzi, Yuri Boykov, and Christopher Schroers · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin · 2018
Cited alongside, same era.
Ocnet: Object context network for scene parsing
Yuhui Yuan and Jingdong Wang · 2018
Cited alongside, same era.
Consistency regularization and cutmix for semi-supervised semantic segmentation
Geoffrey French, Timo Aila, Samuli Laine, Michal Mackiewicz, and Graham D. Finlayson · 2019
Cited alongside, same era.
Closest in time.
Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
Closest in time.
Semi-supervised semantic segmentation via dynamic self-training and class-balanced curriculum
Zhengyang Feng, Qianyu Zhou, Guangliang Cheng, Xin Tan, Jianping Shi, and Lizhuang Ma · 2020
Closest in time.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick · 2020
Closest in time.
Hard negative mixing for contrastive learning
Yannis Kalantidis, Mert Bülent Sariyildiz, Noé Pion, Philippe Weinzaepfel, and Diane Larlus · 2020
Closest in time.
Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
Closest in time.
Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, Richard Socher, and Steven C. H. Hoi · 2020
Closest in time.
Classmix: Segmentation-based data augmentation for semi-supervised learning
Viktor Olsson, Wilhelm Tranheden, Juliano Pinto, and Lennart Svensson · 2020
Closest in time.
Semi-supervised semantic segmentation with cross-consistency training
Yassine Ouali, Céline Hudelot, and Myriam Tami · 2020
Closest in time.
Unsupervised learning of dense visual representations
Pedro O. Pinheiro, Amjad Almahairi, Ryan Y. Benmalek, Florian Golemo, and Aaron C. Courville · 2020
Closest in time.
Contrastive learning with hard negative samples
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka · 2020
Closest in time.
Dense contrastive learning for self-supervised visual pre-training
Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, and Lei Li · 2020
Closest in time.
Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, and Han Hu · 2020
Closest in time.
Object-contextual representations for semantic segmentation
Yuhui Yuan, Xilin Chen, and Jingdong Wang · 2020
Closest in time.
Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, and Quoc Le · 2020
Closest in time.
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
Closest in time.
Exploring cross-image pixel contrast for semantic segmentation
Wenguan Wang, Tianfei Zhou, Fisher Yu, Jifeng Dai, Ender Konukoglu, and Luc Van Gool · 2021
Closest in time.
Detco: Unsupervised contrastive learning for object detection
Enze Xie, Jian Ding, Wenhai Wang, Xiaohang Zhan, Hang Xu, Zhenguo Li, and Ping Luo · 2021
Closest in time.