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Unsupervised domain adaptation (UDA) and domain generalization (DG) enable machine learning models trained on a source domain to perform well on unlabeled or even unseen target domains.
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in CVPR , 2015, pp. 3431–3440
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M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in CVPR , 2016, pp. 3213–3223
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G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez, “The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,” in CVPR , 2016, pp. 3234–3243
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L.-C. Chen, Y. Yang, J. Wang, W. Xu, and A. L. Yuille, “Attention to scale: Scale-aware semantic image segmentation,” in CVPR , 2016, pp. 3640–3649
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Y.-X. Wang, D. Ramanan, and M. Hebert, “Learning to model the tail,” in NeurIPS , 2017, pp. 7032–7042
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A. Tarvainen and H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,” in NeurIPS , 2017, pp. 1195–1204
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H. Zhang, K. Dana, J. Shi, Z. Zhang, X. Wang, A. Tyagi, and A. Agrawal, “Context encoding for semantic segmentation,” in CVPR , 2018, pp. 7151–7160
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R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, “Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness,” in ICLR , 2018
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D. Dai and L. Van Gool, “Dark model adaptation: Semantic image segmentation from daytime to nighttime,” in ITSC , 2018, pp. 3819–3824
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L. Hoyer, M. Munoz, P. Katiyar, A. Khoreva, and V. Fischer, “Grid saliency for context explanations of semantic segmentation,” in NeurIPS , 2019, pp. 6462–6473
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J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, and H. Lu, “Dual attention network for scene segmentation,” in CVPR , 2019, pp. 3146–3154
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D. Lin, D. Shen, S. Shen, Y. Ji, D. Lischinski, D. Cohen-Or, and H. Huang, “Zigzagnet: Fusing top-down and bottom-up context for object segmentation,” in CVPR , 2019, pp. 7490–7499
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D. Hendrycks and T. Dietterich, “Benchmarking neural network robustness to common corruptions and perturbations,” in ICLR , 2019
2019
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X. Yue, Y. Zhang, S. Zhao, A. Sangiovanni-Vincentelli, K. Keutzer, and B. Gong, “Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data,” in ICCV , 2019, pp. 2100–2110
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2019
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T.-H. Vu, H. Jain, M. Bucher, M. Cord, and P. Pérez, “Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,” in CVPR , 2019, pp. 2517–2526
2019
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Y. Zou, Z. Yu, X. Liu, B. Kumar, and J. Wang, “Confidence regularized self-training,” in ICCV , 2019, pp. 5982–5991
2019
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L. Liu, H. Jiang, P. He, W. Chen, X. Liu, J. Gao, and J. Han, “On the variance of the adaptive learning rate and beyond,” in ICLR , 2019
2019
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C. Sakaridis, D. Dai, and L. V. Gool, “Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation,” in ICCV , 2019, pp. 7374–7383
2019
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2019
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Y. Yuan, X. Chen, and J. Wang, “Object-contextual representations for semantic segmentation,” in ECCV , 2020, pp. 173–190
2020
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2020
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C. Kamann and C. Rother, “Benchmarking the robustness of semantic segmentation models with respect to common corruptions,” IJCV , vol. 129, no. 2, pp. 462–483, 2021
2021
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S. Choi, S. Jung, H. Yun, J. T. Kim, S. Kim, and J. Choo, “Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening,” in CVPR , 2021, pp. 11 580–11 590
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D. Peng, Y. Lei, L. Liu, P. Zhang, and J. Liu, “Global and local texture randomization for synthetic-to-real semantic segmentation,” TIP , vol. 30, pp. 6594–6608, 2021
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R. Gong, W. Li, Y. Chen, D. Dai, and L. Van Gool, “Dlow: Domain flow and applications,” IJCV , vol. 129, no. 10, pp. 2865–2888, 2021
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2021
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V. Prabhu, S. Khare, D. Kartik, and J. Hoffman, “Sentry: Selective entropy optimization via committee consistency for unsupervised domain adaptation,” in ICCV , 2021, pp. 8558–8567
2021
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L. Hoyer, D. Dai, Y. Chen, A. Köring, S. Saha, and L. Van Gool, “Three ways to improve semantic segmentation with self-supervised depth estimation,” in CVPR , 2021, pp. 11 130–11 140
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V. Olsson, W. Tranheden, J. Pinto, and L. Svensson, “Classmix: Segmentation-based data augmentation for semi-supervised learning,” in WACV , 2021, pp. 1369–1378
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X. Wu, Z. Wu, H. Guo, L. Ju, and S. Wang, “Dannet: A one-stage domain adaptation network for unsupervised nighttime semantic segmentation,” in CVPR , 2021, pp. 15 769–15 778
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J. Huang, D. Guan, A. Xiao, and S. Lu, “Fsdr: Frequency space domain randomization for domain generalization,” in CVPR , 2021, pp. 6891–6902
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L. Hoyer, D. Dai, and L. Van Gool, “DAFormer: Improving network architectures and training strategies for domain-adaptive semantic segmentation,” in CVPR , 2022
2022
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2022
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Y. Zhao, Z. Zhong, N. Zhao, N. Sebe, and G. H. Lee, “Style-hallucinated dual consistency learning for domain generalized semantic segmentation,” in ECCV , 2022
2022
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Z. Zhong, Y. Zhao, G. H. Lee, and N. Sebe, “Adversarial style augmentation for domain generalized urban-scene segmentation,” in NeurIPS , 2022
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2022
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X. Lai, Z. Tian, X. Xu, Y. Chen, S. Liu, H. Zhao, L. Wang, and J. Jia, “Decouplenet: Decoupled network for domain adaptive semantic segmentation,” in ECCV , 2022, pp. 369–387
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D. Peng, Y. Lei, M. Hayat, Y. Guo, and W. Li, “Semantic-aware domain generalized segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2594–2605
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2022
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L. Hoyer, D. Dai, H. Wang, and L. Van Gool, “MIC: Masked image consistency for context-enhanced domain adaptation,” in CVPR , 2023
2023
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2023
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S. Saha, L. Hoyer, A. Obukhov, D. Dai, and L. Van Gool, “EDAPS: Enhanced domain-adaptive panoptic segmentation,” in ICCV , 2023
2023
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J. Xia, N. Yokoya, B. Adriano, and C. Broni-Bediako, “Openearthmap: A benchmark dataset for global high-resolution land cover mapping,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 6254–6264
2023
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