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Self-training has shown great potential in semi-supervised learning.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
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Semantic contours from inverse detectors
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik · 2011
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Active learning for semantic segmentation with expected change
A. Vezhnevets, J. M. Buhmann, and V. Ferrari · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
D.-H. Lee et al · 2013
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On the importance of initialization and momentum in deep learning
I. Sutskever, J. Martens, G. Dahl, and G. Hinton · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Incorporating nesterov momentum into adam
T. Dozat · 2016
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Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez · 2016
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Nesterov’s accelerated gradient and momentum as approximations to regularised update descent
A. Botev, G. Lever, and D. Barber · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
A. Tarvainen and H. Valpola · 2017
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Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
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Linear coupling: An ultimate unification of gradient and mirror descent
Z. A. Zhu and L. Orecchia · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
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Cycada: Cycle-consistent adversarial domain adaptation
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. Efros, and T. Darrell · 2018
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Learning from synthetic data: Addressing domain shift for semantic segmentation
S. Sankaranarayanan, Y. Balaji, A. Jain, S. N. Lim, and R. Chellappa · 2018
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Learning to adapt structured output space for semantic segmentation
Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang, and M. Chandraker · 2018
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Unified perceptual parsing for scene understanding
T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun · 2018
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Y. Zou, Z. Yu, B. Kumar, and J. Wang · 2018
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All about structure: Adapting structural information across domains for boosting semantic segmentation
W.-L. Chang, H.-P. Wang, W.-H. Peng, and W.-C. Chiu · 2019
Cited alongside, same era.
Progressive feature alignment for unsupervised domain adaptation
C. Chen, W. Xie, W. Huang, Y. Rong, X. Ding, Y. Huang, T. Xu, and J. Huang · 2019
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Dlow: Domain flow for adaptation and generalization
R. Gong, W. Li, Y. Chen, and L. V. Gool · 2019
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Constructing self-motivated pyramid curriculums for cross-domain semantic segmentation: A non-adversarial approach
Q. Lian, F. Lv, L. Duan, and B. Gong · 2019
Cited alongside, same era.
Category anchor-guided unsupervised domain adaptation for semantic segmentation
Q. Zhang, J. Zhang, W. Liu, and D. Tao · 2019
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Pseudo-labeling and confirmation bias in deep semi-supervised learning
Semi-supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank
I. Alonso, A. Sabater, D. Ferstl, L. Montesano, and A. C. Murillo · 2021
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Self-supervised augmentation consistency for adapting semantic segmentation
N. Araslanov and S. Roth · 2021
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Semi-supervised semantic segmentation with cross pseudo supervision
X. Chen, Y. Yuan, G. Zeng, and J. Wang · 2021
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Metacorrection: Domain-aware meta loss correction for unsupervised domain adaptation in semantic segmentation
X. Guo, C. Yang, B. Li, and Y. Yuan · 2021
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Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation
L. Hoyer, D. Dai, and L. Van Gool · 2021
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E. Arazo, D. Ortego, P. Albert, N. E. O’Connor, and K. McGuinness · 2020
Cited alongside, same era.
On the convergence of nesterov’s accelerated gradient method in stochastic settings
M. Assran and M. Rabbat · 2020
Cited alongside, same era.
Semi-supervised semantic segmentation needs strong, varied perturbations
G. French, S. Laine, T. Aila, M. Mackiewicz, and G. D. Finlayson · 2020
Cited alongside, same era.
Bootstrap your own latent-a new approach to self-supervised learning
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, et al · 2020
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
Cited alongside, same era.
Pixel-level cycle association: A new perspective for domain adaptive semantic segmentation
G. Kang, Y. Wei, Y. Yang, Y. Zhuang, and A. Hauptmann · 2020
Cited alongside, same era.
Guided collaborative training for pixel-wise semi-supervised learning
Z. Ke, D. Qiu, K. Li, Q. Yan, and R. W. Lau · 2020
Cited alongside, same era.
Semi-supervised semantic segmentation via adaptive equalization learning
H. Hu, F. Wei, H. Hu, Q. Ye, J. Cui, and L. Wang · 2021
Later among the works it cites.
Semantic distribution-aware contrastive adaptation for semantic segmentation
S. Li, B. Xie, B. Zang, C. H. Liu, X. Cheng, R. Yang, and G. Wang · 2021
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Coarse-to-fine domain adaptive semantic segmentation with photometric alignment and category-center regularization
H. Ma, X. Lin, Z. Wu, and Y. Yu · 2021
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Pixmatch: Unsupervised domain adaptation via pixelwise consistency training
L. Melas-Kyriazi and A. K. Manrai · 2021
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Dacs: Domain adaptation via cross-domain mixed sampling
W. Tranheden, V. Olsson, J. Pinto, and L. Svensson · 2021
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Segformer: Simple and efficient design for semantic segmentation with transformers
E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo · 2021
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End-to-end semi-supervised object detection with soft teacher
M. Xu, Z. Zhang, H. Hu, J. Wang, L. Wang, F. Wei, X. Bai, and Z. Liu · 2021
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Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation
P. Zhang, B. Zhang, T. Zhang, D. Chen, Y. Wang, and F. Wen · 2021
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Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation
Z. Zheng and Y. Yang · 2021
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M.-R. Amini, V. Feofanov, L. Pauletto, E. Devijver, and Y. Maximov · 2022
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Beit: Bert pre-training of image transformers
H. Bao, L. Dong, and F. Wei · 2022
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Debiased pseudo labeling in self-training
B. Chen, J. Jiang, X. Wang, J. Wang, and M. Long · 2022
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Maximizing cosine similarity between spatial features for unsupervised domain adaptation in semantic segmentation
I. Chung, D. Kim, and N. Kwak · 2022
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Unbiased subclass regularization for semi-supervised semantic segmentation
D. Guan, J. Huang, A. Xiao, and S. Lu · 2022
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Class-balanced pixel-level self-labeling for domain adaptive semantic segmentation
R. Li, S. Li, C. He, Y. Zhang, X. Jia, and L. Zhang · 2022
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Bootstrapping semantic segmentation with regional contrast
S. Liu, S. Zhi, E. Johns, and A. J. Davison · 2022
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Semi-supervised semantic segmentation using unreliable pseudo-labels
Y. Wang, H. Wang, Y. Shen, J. Fei, W. Li, G. Jin, L. Wu, R. Zhao, and X. Le · 2022
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M. Wortsman, G. Ilharco, S. Y. Gadre, R. Roelofs, R. Gontijo-Lopes, A. S. Morcos, H. Namkoong, A. Farhadi, Y. Carmon, S. Kornblith, et al · 2022
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Domain adaptive semantic segmentation with regional contrastive consistency regularization
Q. Zhou, C. Zhuang, X. Lu, and L. Ma · 2022
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