Papers › Bidirectional Learning for Domain Adaptation of Semantic Segmentation
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
Yunsheng Li, Lu Yuan, Nuno Vasconcelos
Domain adaptation for semantic image segmentation is very necessary since manually labeling large datasets with pixel-level labels is expensive and time consuming. Existing domain adaptation techniques either work on limited datasets, or yield not so good performance compared with supervised learning. In this paper, we propose a novel bidirectional learning framework for domain adaptation of segmentation. Using the bidirectional learning, the image translation model and the segmentation adaptation model can be learned alternatively and promote to each other. Furthermore, we propose a self-supervised learning algorithm to learn a better segmentation adaptation model and in return improve the image translation model. Experiments show that our method is superior to the state-of-the-art methods in domain adaptation of segmentation with a big margin. The source code is available at https://github.com/liyunsheng13/BDL.
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Code
Syntology Ran 3 of 8 code samples harvested from 1 repository linked to this paper; 5 have no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · our draft was wrong.
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Code Syntology ran Syntology
8 samples harvested; 3 ran; 1 honoured the contract we drafted; 5 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image-to-Image Translation | GTAV-to-Cityscapes Labels | Bidirectional Learning | mIoU | 41.3 | #21 of 22 | Archive leaderboard | report |
| Image-to-Image Translation | SYNTHIA-to-Cityscapes | Bidirectional Learning (ResNet-101) | mIoU (13 classes) | 51.4 | #16 of 28 | Archive leaderboard | report |
| Semantic Segmentation | DADA-seg | BDL | mIoU | 29.66 | #7 of 28 | Archive leaderboard | report |
| Synthetic-to-Real Translation | GTAV-to-Cityscapes Labels | BDL | mIoU | 48.5 | #52 of 73 | 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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