Papers › Semi-supervised semantic segmentation needs strong, varied perturbations
Semi-supervised semantic segmentation needs strong, varied perturbations
Geoff French, Samuli Laine, Timo Aila, Michal Mackiewicz, Graham Finlayson
Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption - under which the data distribution consists of uniform class clusters of samples separated by low density regions - as important to its success. We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success. We then identify choice of augmentation as key to obtaining reliable performance without such low-density regions. We find that adapted variants of the recently proposed CutOut and CutMix augmentation techniques yield state-of-the-art semi-supervised semantic segmentation results in standard datasets. Furthermore, given its challenging nature we propose that semantic segmentation acts as an effective acid test for evaluating semi-supervised regularizers. Implementation at: https://github.com/Britefury/cutmix-semisup-seg.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Semantic Segmentation | ADE20K 1/16 labeled | CutMix | Validation mIoU | 29.8 | #5 of 5 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | ADE20K 1/32 labeled | CutMix | Validation mIoU | 26.2 | #5 of 5 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 100 samples labeled | CutMix (DeepLab v2, ImageNet pre-trained) | Validation mIoU | 51.2 | #13 of 13 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 12.5% labeled | CutMix (DeepLab v2, ImageNet pre-trained) | Validation mIoU | 60.34% | #31 of 33 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 25% labeled | CutMix (DeepLab v2, ImageNet pre-trained) | Validation mIoU | 63.87% | #27 of 30 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 1% labeled | CutMix (DeepLab v3+ ImageNet pre-trained) | Validation mIoU | 59.52% | #4 of 6 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 1% labeled | CutMix (DeepLab v2 ImageNet pre-trained) | Validation mIoU | 53.79% | #6 of 6 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | CutMix | Validation mIoU | 72.45% | #27 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | CutMix | Validation mIoU | 67.6% | #34 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 2% labeled | CutMix (DeepLab v3+ ImageNet pre-trained) | Validation mIoU | 67.05% | #5 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 2% labeled | CutMix (DeepLab v2 ImageNet pre-trained) | Validation mIoU | 64.81% | #8 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 5% labeled | CutMix (DeepLab v3+ ImageNet pre-trained) | Validation mIoU | 69.57% | #6 of 14 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 5% labeled | CutMix (DeepLab v2 ImageNet pre-trained) | Validation mIoU | 66.48% | #12 of 14 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | ScribbleKITTI | CutMix-Seg (Range View) | mIoU (1% Labels) | 36.7 | #5 of 9 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | ScribbleKITTI | CutMix-Seg (Range View) | mIoU (10% Labels) | 50.7 | #5 of 9 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | ScribbleKITTI | CutMix-Seg (Range View) | mIoU (20% Labels) | 52.9 | #5 of 9 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | ScribbleKITTI | CutMix-Seg (Range View) | mIoU (50% Labels) | 54.3 | #5 of 9 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | SemanticKITTI | CutMix-Seg (Range View) | mIoU (1% Labels) | 37.4 | #11 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | SemanticKITTI | CutMix-Seg (Range View) | mIoU (10% Labels) | 54.3 | #11 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | SemanticKITTI | CutMix-Seg (Range View) | mIoU (20% Labels) | 56.6 | #11 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | SemanticKITTI | CutMix-Seg (Range View) | mIoU (50% Labels) | 57.6 | #11 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | nuScenes | CutMix-Seg (Range View) | mIoU (1% Labels) | 43.8 | #7 of 11 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | nuScenes | CutMix-Seg (Range View) | mIoU (10% Labels) | 63.9 | #7 of 11 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | nuScenes | CutMix-Seg (Range View) | mIoU (20% Labels) | 64.8 | #7 of 11 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | nuScenes | CutMix-Seg (Range View) | mIoU (50% Labels) | 69.8 | #7 of 11 | 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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