Fetching the paper…
Reading the bibliography…
Deep learning approaches for semantic segmentation rely primarily on supervised learning approaches and require substantial efforts in producing pixel-level annotations.
Semi-supervised semantic segmentation needs strong, high-dimensional perturbations
French, G.; Aila, T.; Laine, S.; Mackiewicz, M.; and Finlayson, G. 2019 · 1906
Earlier work this paper cites.
MixMatch Domain Adaptaion: Prize-winning solution for both tracks of VisDA 2019 challenge
Rukhovich, D.; and Galeev, D. 2019 · 1910
Earlier work this paper cites.
Category anchor-guided unsupervised domain adaptation for semantic segmentation
Zhang, Q.; Zhang, J.; Liu, W.; and Tao, D. 2019 · 1910
Earlier work this paper cites.
Cycada: Cycle-consistent adversarial domain adaptation
Hoffman, J.; Tzeng, E.; Park, T.; Zhu, J.-Y.; Isola, P.; Saenko, K.; Efros, A.; and Darrell, T. 2018 · 1998
Earlier work this paper cites.
Structured Consistency Loss for semi-supervised semantic segmentation
Kim, J.; Jang, J.; and Park, H. 2020 · 2001
Earlier work this paper cites.
Opposite structure learning for semi-supervised domain adaptation
Qin, C.; Wang, L.; Ma, Q.; Yin, Y.; Wang, H.; and Fu, Y. 2020 · 2002
Earlier work this paper cites.
Semi-Supervised Semantic Segmentation via Dynamic Self-Training and Class-Balanced Curriculum
Feng, Z.; Zhou, Q.; Cheng, G.; Tan, X.; Shi, J.; and Ma, L. 2020 · 2004
Earlier work this paper cites.
Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
Chen, L.-C.; Lopes, R. G.; Cheng, B.; Collins, M. D.; Cubuk, E. D.; Zoph, B.; Adam, H.; and Shlens, J. 2020 · 2005
Earlier work this paper cites.
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
Olsson, V.; Tranheden, W.; Pinto, J.; and Svensson, L. 2020 · 2007
Earlier work this paper cites.
MiCo: Mixup Co-Training for Semi-Supervised Domain Adaptation
Yang, L.; Wang, Y.; Gao, M.; Shrivastava, A.; Weinberger, K. Q.; Chao, W.-L.; and Lim, S.-N. 2020 · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
Earlier work this paper cites.
Co-Training for Domain Adaptation
Chen, M.; Weinberger, K. Q.; and Blitzer, J. 2011 · 2011
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Cordts, M.; Omran, M.; Ramos, S.; Rehfeld, T.; Enzweiler, M.; Benenson, R.; Franke, U.; Roth, S.; and Schiele, B. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
Hoffman, J.; Wang, D.; Yu, F.; and Darrell, T. 2016 · 2016
Earlier work this paper cites.
Playing for data: Ground truth from computer games
Richter, S. R.; Vineet, V.; Roth, S.; and Koltun, V. 2016 · 2016
Cited alongside, same era.
The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
Ros, G.; Sellart, L.; Materzynska, J.; Vazquez, D.; and Lopez, A. M. 2016 · 2016
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; and Yuille, A. L. 2017 · 2017
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A.; and Valpola, H. 2017 · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Zhang, H.; Cisse, M.; Dauphin, Y. N.; and Lopez-Paz, D. 2017 · 2017
Cited alongside, same era.
Semi-supervised domain adaptation via minimax entropy
Saito, K.; Kim, D.; Sclaroff, S.; Darrell, T.; and Saenko, K. 2019 · 2019
Later among the works it cites.
Gated-scnn: Gated shape cnns for semantic segmentation
Takikawa, T.; Acuna, D.; Jampani, V.; and Fidler, S. 2019 · 2019
Later among the works it cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S.; Han, D.; Oh, S. J.; Chun, S.; Choe, J.; and Yoo, Y. 2019 · 2019
Later among the works it cites.
Confidence regularized self-training
Zou, Y.; Yu, Z.; Liu, X.; Kumar, B.; and Wang, J. 2019 · 2019
Later among the works it cites.
Learning texture invariant representation for domain adaptation of semantic segmentation
Kim, M.; and Byun, H. 2020 · 2020
Later among the works it cites.
Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Road: Reality oriented adaptation for semantic segmentation of urban scenes
Chen, Y.; Li, W.; and Van Gool, L. 2018 · 2018
Cited alongside, same era.
Conditional generative adversarial network for structured domain adaptation
Hong, W.; Wang, Z.; Yang, M.; and Yuan, J. 2018 · 2018
Cited alongside, same era.
Adversarial learning for semi-supervised semantic segmentation
Hung, W.-C.; Tsai, Y.-H.; Liou, Y.-T.; Lin, Y.-Y.; and Yang, M.-H. 2018 · 2018
Cited alongside, same era.
Learning to adapt structured output space for semantic segmentation
Tsai, Y.-H.; Hung, W.-C.; Schulter, S.; Sohn, K.; Yang, M.-H.; and Chandraker, M. 2018 · 2018
Cited alongside, same era.
Dcan: Dual channel-wise alignment networks for unsupervised scene adaptation
Wu, Z.; Han, X.; Lin, Y.-L.; Gokhan Uzunbas, M.; Goldstein, T.; Nam Lim, S.; and Davis, L. S. 2018 · 2018
Cited alongside, same era.
Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Zou, Y.; Yu, Z.; Kumar, B.; and Wang, J. 2018 · 2018
Cited alongside, same era.
Bidirectional learning for domain adaptation of semantic segmentation
Li, Y.; Yuan, L.; and Vasconcelos, N. 2019 · 2019
Cited alongside, same era.
Kim, T.; and Kim, C. 2020 · 2020
Later among the works it cites.
Cross-Domain Semantic Segmentation via Domain-Invariant Interactive Relation Transfer
Lv, F.; Liang, T.; Chen, X.; and Lin, G. 2020 · 2020
Later among the works it cites.
Semi-supervised semantic segmentation with cross-consistency training
Ouali, Y.; Hudelot, C.; and Tami, M. 2020 · 2020
Later among the works it cites.
Unsupervised intra-domain adaptation for semantic segmentation through self-supervision
Pan, F.; Shin, I.; Rameau, F.; Lee, S.; and Kweon, I. S. 2020 · 2020
Later among the works it cites.
Alleviating semantic-level shift: A semi-supervised domain adaptation method for semantic segmentation
Wang, Z.; Wei, Y.; Feris, R.; Xiong, J.; Hwu, W.-M.; Huang, T. S.; and Shi, H. 2020 · 2020
Later among the works it cites.
Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation
He, J.; Jia, X.; Chen, S.; and Liu, J. 2021 · 2021
Closest in time.
Classmix: Segmentation-based data augmentation for semi-supervised learning
Olsson, V.; Tranheden, W.; Pinto, J.; and Svensson, L. 2021 · 2021
Closest in time.
Zhang, P.; Zhang, B.; Zhang, T.; Chen, D.; Wang, Y.; and Wen, F. 2021 · 2021
Closest in time.
Domain adaptation for semantic segmentation with maximum squares loss
Chen, M.; Xue, H.; and Cai, D. 2019 · 2099
Closest in time.