Papers › Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a...
Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank
Inigo Alonso, Alberto Sabater, David Ferstl, Luis Montesano, Ana C. Murillo
This work presents a novel approach for semi-supervised semantic segmentation. The key element of this approach is our contrastive learning module that enforces the segmentation network to yield similar pixel-level feature representations for same-class samples across the whole dataset. To achieve this, we maintain a memory bank continuously updated with relevant and high-quality feature vectors from labeled data. In an end-to-end training, the features from both labeled and unlabeled data are optimized to be similar to same-class samples from the memory bank. Our approach outperforms the current state-of-the-art for semi-supervised semantic segmentation and semi-supervised domain adaptation on well-known public benchmarks, with larger improvements on the most challenging scenarios, i.e., less available labeled data. https://github.com/Shathe/SemiSeg-Contrastive
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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 100 samples labeled | SemiSegContrast (DeepLab v3+ with ResNet-50 backbone, MSCOCO pretrained) | Validation mIoU | 64.9% | #4 of 13 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 100 samples labeled | SemiSegContrast (DeepLab v2 with ResNet-101 backbone, MSCOCO pretrained) | Validation mIoU | 59.4% | #7 of 13 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 12.5% labeled | SemiSegContrast (DeepLab v2 with ResNet-101 backbone, MSCOCO pretrained) | Validation mIoU | 64.4% | #27 of 33 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 25% labeled | SemiSegContrast (DeepLab v2 with ResNet-101 backbone, MSCOCO pretrained) | Validation mIoU | 65.9% | #25 of 30 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | SemiSegContrast | Validation mIoU | 71.6% | #28 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 2% labeled | SemiSegContrast (DeepLab v2 with ResNet-101 backbone, MSCOCO pretrained) | Validation mIoU | 67.9% | #2 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 5% labeled | SemiSegContrast (DeepLab v2 with ResNet-101 backbone, MSCOCO pretrained) | Validation mIoU | 70.0% | #4 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.
Methods
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