Papers › Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
Hai-Ming Xu, Lingqiao Liu, Qiuchen Bian, Zhen Yang
Semi-supervised semantic segmentation requires the model to effectively propagate the label information from limited annotated images to unlabeled ones. A challenge for such a per-pixel prediction task is the large intra-class variation, i.e., regions belonging to the same class may exhibit a very different appearance even in the same picture. This diversity will make the label propagation hard from pixels to pixels. To address this problem, we propose a novel approach to regularize the distribution of within-class features to ease label propagation difficulty. Specifically, our approach encourages the consistency between the prediction from a linear predictor and the output from a prototype-based predictor, which implicitly encourages features from the same pseudo-class to be close to at least one within-class prototype while staying far from the other between-class prototypes. By further incorporating CutMix operations and a carefully-designed prototype maintenance strategy, we create a semi-supervised semantic segmentation algorithm that demonstrates superior performance over the state-of-the-art methods from extensive experimental evaluation on both Pascal VOC and Cityscapes benchmarks.
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Code
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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 | PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | Validation mIoU | 76.31% | #17 of 33 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 25% labeled | PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | Validation mIoU | 78.4% | #15 of 30 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | Validation mIoU | 79.11% | #15 of 23 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 6.25% labeled | PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | Validation mIoU | 73.41% | #14 of 18 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | PASCAL VOC 2012 25% labeled | PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | Validation mIoU | 80.78 | #7 of 27 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | PASCAL VOC 2012 50% | PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | Validation mIoU | 80.91% | #2 of 14 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | Validation mIoU | 80.71% | #8 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 6.25% labeled | PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | Validation mIoU | 78.6 | #9 of 19 | 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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