mlsanityData to 7 Oct 2026

n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation

arXiv 2112.07528 ↗First posted 14 Dec 2021cs.CV cs.AI cs.LGcited by 10

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Abstract (arXiv)

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).

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 14 Dec 2021.

Builds on

15 of 30 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  4. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  5. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  6. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  7. 2016Pyramid Scene Parsing Networkcited by 2,472
  8. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  9. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,417
  10. 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 577
  11. 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 462
  12. 2020Semi-Supervised Semantic Segmentation with Cross-Consistency Trainingcited by 375
3 more
  1. 2020Guided Collaborative Training for Pixel-wise Semi-Supervised Learningcited by 120
  2. 2021Bootstrapping Semantic Segmentation with Regional Contrastcited by 80
  3. 2020DMT: Dynamic Mutual Training for Semi-Supervised Learningcited by 77

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 2; 2025: 1; 2026: 1.

10 papers.

  1. 2023Diverse Cotraining Makes Strong Semi-Supervised Segmentorcited by 26
  2. 2022Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Studentscited by 16
  3. 2024Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Surveycited by 12
  4. 2022SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D datacited by 6
  5. 2022Progressive Learning with Cross-Window Consistency for Semi-Supervised Semantic Segmentationcited by 3
  6. 2023Confidence-Guided Semi-supervised Learning in Land Cover Classificationcited by 3
  7. 2024Knowledge Distillation for Road Detection based on cross-model Semi-Supervised Learningcited by 2
  8. 2025Integrating Semi-Supervised and Active Learning for Semantic Segmentationcited by 2
  9. 2023DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagerycited by 1
  10. 2026Semi-Supervised Learning for Molecular Graphs via Ensemble Consensuscited by 0