Papers › Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive...
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
Pan Zhang, Bo Zhang, Ting Zhang, Dong Chen, Yong Wang, Fang Wen
Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels are noisy and the target features are dispersed due to the discrepancy between source and target domains. In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation. In particular, we take one step further and exploit the feature distances from prototypes that provide richer information than mere prototypes. Specifically, we use it to estimate the likelihood of pseudo labels to facilitate online correction in the course of training. Meanwhile, we align the prototypical assignments based on relative feature distances for two different views of the same target, producing a more compact target feature space. Moreover, we find that distilling the already learned knowledge to a self-supervised pretrained model further boosts the performance. Our method shows tremendous performance advantage over state-of-the-art methods. We will make the code publicly available.
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Code
Syntology Ran 8 of 17 code samples harvested from 1 repository linked to this paper; 9 have no recorded run. Of those that ran: 2 ran · our draft was wrong; 6 ran with no contract checked.
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Code Syntology ran Syntology
17 samples harvested; 8 ran; 0 honoured the contract we drafted; 9 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 |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | GTA5 to Cityscapes | ProDA | mIoU | 57.5 | #21 of 28 | Archive leaderboard | report |
| Image-to-Image Translation | GTAV-to-Cityscapes Labels | ProDA | mIoU | 57.5 | #15 of 22 | Archive leaderboard | report |
| Image-to-Image Translation | SYNTHIA-to-Cityscapes | ProDA | mIoU (13 classes) | 62.0 | #11 of 28 | Archive leaderboard | report |
| Semantic Segmentation | GTAV-to-Cityscapes Labels | ProDA | mIoU | 57.5 | #10 of 12 | Archive leaderboard | report |
| Synthetic-to-Real Translation | GTAV-to-Cityscapes Labels | ProDA(with knowledge distillation) | mIoU | 57.5 | #24 of 73 | Archive leaderboard | report |
| Synthetic-to-Real Translation | SYNTHIA-to-Cityscapes | ProDA(ResNet-101) | MIoU (13 classes) | 62.0 | #16 of 38 | Archive leaderboard | report |
| Synthetic-to-Real Translation | SYNTHIA-to-Cityscapes | ProDA(ResNet-101) | MIoU (16 classes) | 55.5 | #16 of 38 | 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.
Methods
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