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Domain adaptation is of huge interest as labeling is an expensive and error-prone task, especially when labels are needed on pixel-level like in semantic segmentation.
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2019
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2019
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M. Kim and H. Byun, “Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 12 975–12 984
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2020
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L. Cai, X. Xu, J. H. Liew, and C. S. Foo, “Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 988–10 997
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2021
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2022
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S. Brehm, S. Scherer, and R. Lienhart, “Semantically consistent image-to-image translation for unsupervised domain adaptation,” ICAART , vol. 2, pp. 131–141, 2022
2022
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2022
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S. Harary, E. Schwartz, A. Arbelle, P. Staar, S. Abu-Hussein, E. Amrani, R. Herzig, A. Alfassy, R. Giryes, and H. Kuehne, “Unsupervised Domain Generalization by Learning a Bridge Across Domains,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5280–5290
2022
Closest in time.