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A Novel Unsupervised Domain Adaption Method for Depth-Guided Semantic Segmentation Using Coarse-to-Fine Alignment

13 Sep 2022IEEE Access 2022 9archive 2025-07-28

Kieu Dang Nam, Nguyen Minh Tu, Trinh Van Dieu, Muriel Visani, Nguyen Thi-Oanh, Dinh Viet Sang

Domain adaptation methods in machine learning deal with the domain shift issue by aligning source and target data representation. This paper proposes a novel domain adaptation method for semantic segmentation that exploits the Fourier transform on chromatic space to improve the quality of style transfer, and generates pseudo-labels for self-training by combining the results from different teachers obtained at different rounds of self-training. Our method also applies class-level adversarial learning to achieve a more fine-grained alignment between the two domains, and a late fusion with a depth-estimation model to improve its segmentation outputs. Experiments show that our method yields superior performance in terms of accuracy compared to other existing state-of-the-art methods.

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Tasks

Depth EstimationDomain AdaptationSegmentationSemantic SegmentationStyle TransferUnsupervised Domain Adaptation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Domain Adaptation GTA5 to Cityscapes FAFS mIoU 58.8 #19 of 28 Archive leaderboard report
Unsupervised Domain Adaptation GTAV-to-Cityscapes Labels FAFS mIoU 58.8 #14 of 20 Archive leaderboard report
Unsupervised Domain Adaptation SYNTHIA-to-Cityscapes FAFS mIoU 54.5 #14 of 23 Archive leaderboard report
Unsupervised Domain Adaptation SYNTHIA-to-Cityscapes FAFS mIoU (13 classes) 61.4 #14 of 23 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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