Papers › A Novel Unsupervised Domain Adaption Method for Depth-Guided Semantic Segmentation...
A Novel Unsupervised Domain Adaption Method for Depth-Guided Semantic Segmentation Using Coarse-to-Fine Alignment
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
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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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