Papers › Semi-Supervised Semantic Segmentation with High- and Low-level Consistency
Semi-Supervised Semantic Segmentation with High- and Low-level Consistency
Sudhanshu Mittal, Maxim Tatarchenko, Thomas Brox
The ability to understand visual information from limited labeled data is an important aspect of machine learning. While image-level classification has been extensively studied in a semi-supervised setting, dense pixel-level classification with limited data has only drawn attention recently. In this work, we propose an approach for semi-supervised semantic segmentation that learns from limited pixel-wise annotated samples while exploiting additional annotation-free images. It uses two network branches that link semi-supervised classification with semi-supervised segmentation including self-training. The dual-branch approach reduces both the low-level and the high-level artifacts typical when training with few labels. The approach attains significant improvement over existing methods, especially when trained with very few labeled samples. On several standard benchmarks - PASCAL VOC 2012, PASCAL-Context, and Cityscapes - the approach achieves new state-of-the-art in semi-supervised learning.
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Code
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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 12.5% labeled | s4GAN (DeepLab v2 ImageNet pre-trained) | Validation mIoU | 59.3% | #32 of 33 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 2% labeled | S4GAN (DeepLabv2 with ResNet101, MSCOCO pre-trained) | Validation mIoU | 50.48% | #3 of 3 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 25% labeled | s4GAN (DeepLab v2 ImageNet pre-trained) | Validation mIoU | 61.9% | #29 of 30 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 5% labeled | S4GAN (DeepLabv2 with ResNet101, MSCOCO pre-trained) | Validation mIoU | 55.61% | #3 of 3 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | PASCAL Context 12.5% labeled | s4GAN+MLMT (DeepLab v2 ImageNet pre-trained) | Validation mIoU | 35.3 | #2 of 2 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | PASCAL Context 25% labeled | s4GAN+MLMT (DeepLab v2 ImageNet pre-trained) | Validation mIoU | 37.8 | #2 of 2 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | s4GAN + MLMT | Validation mIoU | 71.4% | #29 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | s4GAN+MLMT | Validation mIoU | 70.4% | #33 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | s4GAN+MLMT | Validation mIoU | 67.3% | #35 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 2% labeled | s4GAN + MLMT (DeepLab v2 MSCOCO/ImageNet pre-trained) | Validation mIoU | 63.3% | #9 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 2% labeled | s4GAN+MLMT (DeepLab v3+ ImageNet pre-trained) | Validation mIoU | 62.6% | #10 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 2% labeled | s4GAN+MLMT (DeepLab v2 ImageNet pre-trained) | Validation mIoU | 60.4% | #11 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 5% labeled | s4GAN + MLMT (DeepLab v2 MSCOCO/ImageNet pre-trained) | Validation mIoU | 67.2% | #10 of 14 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 5% labeled | s4GAN+MLMT (DeepLab v3+ ImageNet pre-trained) | Validation mIoU | 66.6% | #11 of 14 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 5% labeled | s4GAN+MLMT (DeepLab v2 ImageNet pre-trained) | Validation mIoU | 62.9% | #13 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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