Papers › n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised...
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation
Dominik Filipiak, Piotr Tempczyk, Marek Cygan
We present n-CPS - a generalisation of the recent state-of-the-art cross pseudo supervision (CPS) approach for the task of semi-supervised semantic segmentation. In n-CPS, there are n simultaneously trained subnetworks that learn from each other through one-hot encoding perturbation and consistency regularisation. We also show that ensembling techniques applied to subnetworks outputs can significantly improve the performance. To the best of our knowledge, n-CPS paired with CutMix outperforms CPS and sets the new state-of-the-art for Pascal VOC 2012 with (1/16, 1/8, 1/4, and 1/2 supervised regimes) and Cityscapes (1/16 supervised).
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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 | n-CPS (ResNet-50) | Validation mIoU | 77.61% | #12 of 33 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 25% labeled | n-CPS (ResNet-50) | Validation mIoU | 78.41% | #14 of 30 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | n-CPS (ResNet-50) | Validation mIoU | 79.29% | #11 of 23 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 6.25% labeled | n-CPS (ResNet-50) | Validation mIoU | 76.08 | #9 of 18 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | PASCAL VOC 2012 25% labeled | n-CPS (ResNet-101) | Validation mIoU | 78.97 | #12 of 27 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | PASCAL VOC 2012 25% labeled | n-CPS (ResNet-50) | Validation mIoU | 75.85 | #21 of 27 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | PASCAL VOC 2012 50% | n-CPS (ResNet-101) | Validation mIoU | 80.26% | #5 of 14 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | PASCAL VOC 2012 50% | n-CPS (ResNet-50) | Validation mIoU | 77.07% | #10 of 14 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | n-CPS (ResNet-101) | Validation mIoU | 77.99% | #13 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | n-CPS | Validation mIoU | 74.21% | #22 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 6.25% labeled | n-CPS (ResNet-101) | Validation mIoU | 75.86 | #13 of 19 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 6.25% labeled | n-CPS (ResNet-50) | Validation mIoU | 72.03 | #15 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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