Papers › ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
Viktor Olsson, Wilhelm Tranheden, Juliano Pinto, Lennart Svensson
The state of the art in semantic segmentation is steadily increasing in performance, resulting in more precise and reliable segmentations in many different applications. However, progress is limited by the cost of generating labels for training, which sometimes requires hours of manual labor for a single image. Because of this, semi-supervised methods have been applied to this task, with varying degrees of success. A key challenge is that common augmentations used in semi-supervised classification are less effective for semantic segmentation. We propose a novel data augmentation mechanism called ClassMix, which generates augmentations by mixing unlabelled samples, by leveraging on the network's predictions for respecting object boundaries. We evaluate this augmentation technique on two common semi-supervised semantic segmentation benchmarks, showing that it attains state-of-the-art results. Lastly, we also provide extensive ablation studies comparing different design decisions and training regimes.
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Code
Syntology Ran 5 of 19 code samples harvested from 1 repository linked to this paper; 14 have no recorded run. Of those that ran: 3 ran · honoured contract; 1 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
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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 | Cityscapes 100 samples labeled | ClassMix (DeepLab v2 MSCOCO pretrained) | Validation mIoU | 54.07% | #12 of 13 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 12.5% labeled | ClassMix (DeepLab v2 MSCOCO pretrained) | Validation mIoU | 61.35% | #30 of 33 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 2% labeled | ClassMix (DeepLabv2 with ResNet101, MSCOCO pre-trained) | Validation mIoU | 52.14% | #2 of 3 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 25% labeled | ClassMix (DeepLab v2 MSCOCO pretrained) | Validation mIoU | 63.63% | #28 of 30 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 5% labeled | ClassMix (DeepLabv2 with ResNet101, MSCOCO pre-trained) | Validation mIoU | 58.77% | #2 of 3 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | ClassMix (DeepLab v2 MSCOCO pretrained) | Validation mIoU | 66.29% | #22 of 23 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | PASCAL VOC 2012 25% labeled | ClassMix (DeepLab v2 MSCOCO pretrained) | Validation mIoU | 72.45 | #25 of 27 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 1% labeled | ClassMix (DeepLab v2 MSCOCO pretrained) | Validation mIoU | 54.18% | #5 of 6 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | ClassMix | Validation mIoU | 71.00% | #30 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 2% labeled | ClassMix (DeepLab v2 MSCOCO pretrained) | Validation mIoU | 66.15% | #7 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 5% labeled | ClassMix (DeepLab v2 MSCOCO pretrained) | Validation mIoU | 67.77% | #9 of 14 | 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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