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Source-free domain adaptation has developed rapidly in recent years, where the well-trained source model is adapted to the target domain instead of the source data, offering the potential for privacy concerns and intellectual property protection.
Learning and evaluating classifiers under sample selection bias
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Direct importance estimation with model selection and its application to covariate shift adaptation
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A survey on transfer learning
S. J. Pan and Q. Yang · 2009
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Self-paced learning for latent variable models
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Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation
B. Gong, K. Grauman, and F. Sha · 2013
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Generative adversarial nets
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Domain adaptation for face recognition: Targetize source domain bridged by common subspace
M. Kan, J. Wu, S. Shan, and X. Chen · 2014
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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
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Distilling the knowledge in a neural network
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Fully convolutional networks for semantic segmentation
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Domain separation networks
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
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Domain adaptation in the absence of source domain data
B. Chidlovskii, S. Clinchant, and G. Csurka · 2016
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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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Deep reconstruction-classification networks for unsupervised domain adaptation
M. Ghifary, W. B. Kleijn, M. Zhang, D. Balduzzi, and W. Li · 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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Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
J. Hoffman, D. Wang, F. Yu, and T. Darrell · 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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No more discrimination: Cross city adaptation of road scene segmenters
Y.-H. Chen, W.-Y. Chen, Y.-T. Chen, B.-C. Tsai, Y.-C. Frank Wang, and M. Sun · 2017
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Self-paced learning: An implicit regularization perspective
Y. Fan, R. He, J. Liang, and B. Hu · 2017
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Autodial: Automatic domain alignment layers
F. Maria Carlucci, L. Porzi, B. Caputo, E. Ricci, and S. Rota Bulo · 2017
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Semi supervised semantic segmentation using generative adversarial network
N. Souly, C. Spampinato, and M. Shah · 2017
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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Deep hashing network for unsupervised domain adaptation
H. Venkateswara, J. Eusebio, S. Chakraborty, and S. Panchanathan · 2017
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Curriculum domain adaptation for semantic segmentation of urban scenes
Y. Zhang, P. David, and B. Gong · 2017
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Domain adaptive faster r-cnn for object detection in the wild
Y. Chen, W. Li, C. Sakaridis, D. Dai, and L. Van Gool · 2018
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Self-ensembling for visual domain adaptation
G. French, M. Mackiewicz, and M. Fisher · 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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Conditional generative adversarial network for structured domain adaptation
W. Hong, Z. Wang, M. Yang, and J. Yuan · 2018
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Domain transfer through deep activation matching
H. Huang, Q. Huang, and P. Krahenbuhl · 2018
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Aggregating randomized clustering-promoting invariant projections for domain adaptation
J. Liang, R. He, Z. Sun, and T. Tan · 2018
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Transferable representation learning with deep adaptation networks
M. Long, Y. Cao, Z. Cao, J. Wang, and M. I. Jordan · 2018
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Conditional adversarial domain adaptation
M. Long, Z. Cao, J. Wang, and M. I. Jordan · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
T. Miyato, S.-i. Maeda, M. Koyama, and S. Ishii · 2018
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Image to image translation for domain adaptation
Z. Murez, S. Kolouri, D. Kriegman, R. Ramamoorthi, and K. Kim · 2018
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Multi-adversarial domain adaptation
Z. Pei, Z. Cao, M. Long, and J. Wang · 2018
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Visda: A synthetic-to-real benchmark for visual domain adaptation
X. Peng, B. Usman, N. Kaushik, D. Wang, J. Hoffman, and K. Saenko · 2018
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Adversarial dropout regularization
K. Saito, Y. Ushiku, T. Harada, and K. Saenko · 2018
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Model adaptation with synthetic and real data for semantic dense foggy scene understanding
C. Sakaridis, D. Dai, S. Hecker, and L. Van Gool · 2018
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Semantic foggy scene understanding with synthetic data
C. Sakaridis, D. Dai, and L. Van Gool · 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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Dcan: Dual channel-wise alignment networks for unsupervised scene adaptation
Z. Wu, X. Han, Y.-L. Lin, M. Gokhan Uzunbas, T. Goldstein, S. Nam Lim, and L. S. Davis · 2018
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Penalizing top performers: Conservative loss for semantic segmentation adaptation
X. Zhu, H. Zhou, C. Yang, J. Shi, and D. Lin · 2018
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Y. Zou, Z. Yu, B. Vijaya Kumar, and J. Wang · 2018
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Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring
D. Berthelot, N. Carlini, E. D. Cubuk, A. Kurakin, K. Sohn, H. Zhang, and C. Raffel · 2019
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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
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Domain adaptation for semantic segmentation with maximum squares loss
M. Chen, H. Xue, and D. Cai · 2019
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Crdoco: Pixel-level domain transfer with cross-domain consistency
Y.-C. Chen, Y.-Y. Lin, M.-H. Yang, and J.-B. Huang · 2019
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Self-ensembling with gan-based data augmentation for domain adaptation in semantic segmentation
J. Choi, T. Kim, and C. Kim · 2019
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Unsupervised domain adaptation via regularized conditional alignment
S. Cicek and S. Soatto · 2019
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Ssf-dan: Separated semantic feature based domain adaptation network for semantic segmentation
L. Du, J. Tan, H. Yang, J. Feng, X. Xue, Q. Zheng, X. Ye, and X. Zhang · 2019
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Adversarial learning for semi-supervised semantic segmentation
W. C. Hung, Y. H. Tsai, Y. T. Liou, Y. Y. Lin, and M. H. Yang · 2019
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Looking back at labels: A class based domain adaptation technique
V. K. Kurmi and V. P. Namboodiri · 2019
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Drop to adapt: Learning discriminative features for unsupervised domain adaptation
S. Lee, D. Kim, N. Kim, and S.-G. Jeong · 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
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Distant supervised centroid shift: A simple and efficient approach to visual domain adaptation
J. Liang, R. He, Z. Sun, and T. Tan · 2019
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Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation
Y. Luo, L. Zheng, T. Guan, J. Yu, and Y. Yang · 2019
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Semi-supervised semantic segmentation with high-and low-level consistency
S. Mittal, M. Tatarchenko, and T. Brox · 2019
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Domain adaptation for structured output via discriminative patch representations
Y.-H. Tsai, K. Sohn, S. Schulter, and M. Chandraker · 2019
Cited alongside, same era.
Interpolation consistency training for semi-supervised learning
V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz · 2019
Cited alongside, same era.
Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation
T.-H. Vu, H. Jain, M. Bucher, M. Cord, and P. Pérez · 2019
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Transferable attention for domain adaptation
Uncertainty-aware pseudo label refinery for domain adaptive semantic segmentation
Y. Wang, J. Peng, and Z. Zhang · 2021
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Adaptive adversarial network for source-free domain adaptation
H. Xia, H. Zhao, and Z. Ding · 2021
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Adaptive adversarial network for source-free domain adaptation
H. Xia, H. Zhao, and Z. Ding · 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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Augmented self-labeling for source-free unsupervised domain adaptation
H. Yan, Y. Guo, and C. Yang · 2021
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Exploring robustness of unsupervised domain adaptation in semantic segmentation
J. Yang, C. Li, W. An, H. Ma, Y. Guo, Y. Rong, P. Zhao, and J. Huang · 2021
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X. Wang, L. Li, W. Ye, M. Long, and J. Wang · 2019
Cited alongside, same era.
Category anchor-guided unsupervised domain adaptation for semantic segmentation
Q. Zhang, J. Zhang, W. Liu, and D. Tao · 2019
Cited alongside, same era.
Confidence regularized self-training
Y. Zou, Z. Yu, X. Liu, B. Kumar, and J. Wang · 2019
Cited alongside, same era.
Curriculum model adaptation with synthetic and real data for semantic foggy scene understanding
D. Dai, C. Sakaridis, S. Hecker, and L. Van Gool · 2020
Cited alongside, same era.
Self-paced contrastive learning with hybrid memory for domain adaptive object re-id
Y. Ge, F. Zhu, D. Chen, R. Zhao, et al · 2020
Cited alongside, same era.
Spherical space domain adaptation with robust pseudo-label loss
X. Gu, J. Sun, and Z. Xu · 2020
Cited alongside, same era.
Contextual-relation consistent domain adaptation for semantic segmentation
J. Huang, S. Lu, D. Guan, and X. Zhang · 2020
Cited alongside, same era.
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St3d: Self-training for unsupervised domain adaptation on 3d object detection
J. Yang, S. Shi, Z. Wang, H. Li, and X. Qi · 2021
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Generalized source-free domain adaptation
S. Yang, Y. Wang, J. van de Weijer, L. Herranz, and S. Jui · 2021
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Generalized source-free domain adaptation
S. Yang, Y. Wang, J. Van De Weijer, L. Herranz, and S. Jui · 2021
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Source data-free unsupervised domain adaptation for semantic segmentation
M. Ye, J. Zhang, J. Ouyang, and D. Yuan · 2021
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Domain adaptive semantic segmentation without source data
F. You, J. Li, L. Zhu, Z. Chen, and Z. Huang · 2021
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A simple baseline for semi-supervised semantic segmentation with strong data augmentation
J. Yuan, Y. Liu, C. Shen, Z. Wang, and H. Li · 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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Contrastive test-time adaptation
D. Chen, D. Wang, T. Darrell, and S. Ebrahimi · 2022
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Smoothing matters: Momentum transformer for domain adaptive semantic segmentation
R. Chen, Y. Rong, S. Guo, J. Han, F. Sun, T. Xu, and W. Huang · 2022
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Source-free domain adaptation via distribution estimation
N. Ding, Y. Xu, Y. Tang, C. Xu, Y. Wang, and D. Tao · 2022
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Toward few-shot domain adaptation with perturbation-invariant representation and transferable prototypes
J. Fan, Y. Wang, H. Guan, C. Song, and Z. Zhang · 2022
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Interact with open scenes: A life-long evolution framework for interactive segmentation models
R. Gan, J. Fan, Y. Wang, and Z. Zhang · 2022
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Visual prompt tuning for test-time domain adaptation
Y. Gao, X. Shi, Y. Zhu, H. Wang, Z. Tang, X. Zhou, M. Li, and D. N. Metaxas · 2022
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Simt: Handling open-set noise for domain adaptive semantic segmentation
X. Guo, J. Liu, T. Liu, and Y. Yuan · 2022
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Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation
L. Hoyer, D. Dai, and L. Van Gool · 2022
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Hrda: Context-aware high-resolution domain-adaptive semantic segmentation
L. Hoyer, D. Dai, and L. Van Gool · 2022
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Dine: Domain adaptation from single and multiple black-box predictors
J. Liang, D. Hu, J. Feng, and R. He · 2022
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Unsupervised black-box model domain adaptation for brain tumor segmentation
X. Liu, C. Yoo, F. Xing, C.-C. J. Kuo, G. El Fakhri, J.-W. Kang, and J. Woo · 2022
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Learning to adapt across dual discrepancy for cross-domain person re-identification
C. Luo, C. Song, and Z. Zhang · 2022
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I2f: A unified image-to-feature approach for domain adaptive semantic segmentation
H. Ma, X. Lin, and Y. Yu · 2022
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Augmentation consistency-guided self-training for source-free domain adaptive semantic segmentation
V. U. Prabhu, S. Khare, D. Kartik, and J. Hoffman · 2022
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Continual test-time domain adaptation
Q. Wang, O. Fink, L. Van Gool, and D. Dai · 2022
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Remember the difference: Cross-domain few-shot semantic segmentation via meta-memory transfer
W. Wang, L. Duan, Y. Wang, Q. En, J. Fan, and Z. Zhang · 2022
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Source free domain adaptation for semantic segmentation via distribution transfer and adaptive class-balanced self-training
C.-Y. Yang, Y.-J. Kuo, and C.-T. Hsu · 2022
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When source-free domain adaptation meets learning with noisy labels
L. Yi, G. Xu, P. Xu, J. Li, R. Pu, C. Ling, I. McLeod, and B. Wang · 2022
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Decorate the newcomers: Visual domain prompt for continual test time adaptation
Y. Gan, Y. Bai, Y. Lou, X. Ma, R. Zhang, N. Shi, and L. Luo · 2023
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Mic: Masked image consistency for context-enhanced domain adaptation
L. Hoyer, D. Dai, H. Wang, and L. Van Gool · 2023
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C-sfda: A curriculum learning aided self-training framework for efficient source free domain adaptation
N. Karim, N. C. Mithun, A. Rajvanshi, H.-p. Chiu, S. Samarasekera, and N. Rahnavard · 2023
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Coarse mask guided interactive object segmentation
J. Li, J. Fan, Y. Wang, Y. Yang, and Z. Zhang · 2023
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Vblc: visibility boosting and logit-constraint learning for domain adaptive semantic segmentation under adverse conditions
M. Li, B. Xie, S. Li, C. H. Liu, and X. Cheng · 2023
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A comprehensive survey on test-time adaptation under distribution shifts
J. Liang, R. He, and T. Tan · 2023
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Guiding pseudo-labels with uncertainty estimation for source-free unsupervised domain adaptation
M. Litrico, A. Del Bue, and P. Morerio · 2023
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When visual prompt tuning meets source-free domain adaptive semantic segmentation
X. Ma, Y. Wang, H. Liu, T. Guo, and Y. Wang · 2023
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Gaia-universe: Everything is super-netify
J. Peng, Q. Chang, H. Yin, X. Bu, J. Sun, L. Xie, X. Zhang, Q. Tian, and Z. Zhang · 2023
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Droppos: pre-training vision transformers by reconstructing dropped positions
H. Wang, J. Fan, Y. Wang, K. Song, T. Wang, and Z. Zhang · 2023
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Pulling target to source: A new perspective on domain adaptive semantic segmentation
H. Wang, Y. Shen, J. Fei, W. Li, L. Wu, Y. Wang, and Z. Zhang · 2023
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Hard patches mining for masked image modeling
H. Wang, K. Song, J. Fan, Y. Wang, J. Xie, and Z. Zhang · 2023
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Using unreliable pseudo-labels for label-efficient semantic segmentation
H. Wang, Y. Wang, Y. Shen, J. Fan, Y. Wang, and Z. Zhang · 2023
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A survey of deep visual cross-domain few-shot learning
W. Wang, L. Duan, Y. Wang, J. Fan, Z. Gong, and Z. Zhang · 2023
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Mmt: Cross domain few-shot learning via meta-memory transfer
W. Wang, L. Duan, Y. Wang, J. Fan, and Z. Zhang · 2023
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Informative data mining for one-shot cross-domain semantic segmentation
Y. Wang, J. Liang, J. Xiao, S. Mei, Y. Yang, and Z. Zhang · 2023
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Cal-sfda: Source-free domain-adaptive semantic segmentation with differentiable expected calibration error
Z. Wang, Y. Luo, Z. Chen, S. Wang, and Z. Huang · 2023
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P2t: Pyramid pooling transformer for scene understanding
Y.-H. Wu, Y. Liu, X. Zhan, and M.-M. Cheng · 2023
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Sepico: Semantic-guided pixel contrast for domain adaptive semantic segmentation
B. Xie, S. Li, M. Li, C. H. Liu, G. Huang, and G. Wang · 2023
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Trust your good friends: Source-free domain adaptation by reciprocal neighborhood clustering
S. Yang, Y. Wang, J. van de Weijer, L. Herranz, S. Jui, and J. Yang · 2023
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Black-box unsupervised domain adaptation with bi-directional atkinson-shiffrin memory
J. Zhang, J. Huang, X. Jiang, and S. Lu · 2023
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Towards better stability and adaptability: Improve online self-training for model adaptation in semantic segmentation
D. Zhao, S. Wang, Q. Zang, D. Quan, X. Ye, and L. Jiao · 2023
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Towards source-free domain adaptive semantic segmentation via importance-aware and prototype-contrast learning
Y. Cao, H. Zhang, X. Lu, Z. Xiao, K. Yang, and Y. Wang · 2024
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Domain adaptive and generalizable network architectures and training strategies for semantic image segmentation
L. Hoyer, D. Dai, and L. Van Gool · 2024
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Source-free domain adaptation for rgb-d semantic segmentation with vision transformers
G. Rizzoli, D. Shenaj, and P. Zanuttigh · 2024
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