Papers › FDA: Fourier Domain Adaptation for Semantic Segmentation
FDA: Fourier Domain Adaptation for Semantic Segmentation
Yanchao Yang, Stefano Soatto
We describe a simple method for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectrum of one with the other. We illustrate the method in semantic segmentation, where densely annotated images are aplenty in one domain (synthetic data), but difficult to obtain in another (real images). Current state-of-the-art methods are complex, some requiring adversarial optimization to render the backbone of a neural network invariant to the discrete domain selection variable. Our method does not require any training to perform the domain alignment, just a simple Fourier Transform and its inverse. Despite its simplicity, it achieves state-of-the-art performance in the current benchmarks, when integrated into a relatively standard semantic segmentation model. Our results indicate that even simple procedures can discount nuisance variability in the data that more sophisticated methods struggle to learn away.
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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 | Cityscapes to ACDC | FDA (DeepLabv2) | mIoU | 45.7 | #15 of 16 | Archive leaderboard | report |
| Domain Adaptation | Panoptic SYNTHIA-to-Mapillary | FDA | mPQ | 19.1 | #4 of 5 | Archive leaderboard | report |
| Domain Adaptation | SYNTHIA-to-Cityscapes | FDA (VGG-16) | mIoU | 40.5 | #30 of 33 | Archive leaderboard | report |
| Semantic Segmentation | DADA-seg | FDA | mIoU | 24.45 | #15 of 28 | 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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