mlsanityData to 7 Oct 2026

Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey

arXiv 2403.01909 ↗First posted 4 Mar 2024cs.CV cs.AIcited by 12

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Abstract (arXiv)

Semantic segmentation is an important and popular research area in computer vision that focuses on classifying pixels in an image based on their semantics. However, supervised deep learning requires large amounts of data to train models and the process of labeling images pixel by pixel is time-consuming and laborious. This review aims to provide a first comprehensive and organized overview of the state-of-the-art research results on pseudo-label methods in the field of semi-supervised semantic segmentation, which we categorize from different perspectives and present specific methods for specific application areas. In addition, we explore the application of pseudo-label technology in medical and remote-sensing image segmentation. Finally, we also propose some feasible future research directions to address the existing challenges.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 130 of its 239 references that are in the map. First posted on arXiv 4 Mar 2024.

Builds on

130 of 239 references.

References · 130

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  6. 2023Segment Anythingcited by 8,158
  7. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  8. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  9. 2017Mask R-CNNcited by 7,259
  10. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  11. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  12. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
38 more
  1. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  2. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  3. 2021Learning to Prompt for Vision-Language Modelscited by 2,269
  4. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  5. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  6. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,488
  7. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  8. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
  9. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,282
  10. 2018Unified Perceptual Parsing for Scene Understandingcited by 1,265
  11. 2016Semantic Understanding of Scenes through the ADE20K Datasetcited by 1,063
  12. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,057
  13. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,004
  14. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 918
  15. 2022MaPLe: Multi-modal Prompt Learningcited by 685
  16. 2020Image Segmentation Using Deep Learning: A Surveycited by 659
  17. 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 657
  18. 2017Deep Mutual Learningcited by 621
  19. 2021The Medical Segmentation Decathloncited by 620
  20. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  21. 2019Object Detection in 20 Years: A Surveycited by 528
  22. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 507
  23. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 504
  24. 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 462
  25. 2020Semi-Supervised Semantic Segmentation with Cross-Consistency Trainingcited by 375
  26. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 371
  27. 2021End-to-End Semi-Supervised Object Detection with Soft Teachercited by 315
  28. 2017Relation Networks for Object Detectioncited by 305
  29. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
  30. 2020A Brief Review of Domain Adaptationcited by 287
  31. 2014Learning with Pseudo-Ensemblescited by 241
  32. 2022FreeMatch: Self-adaptive Thresholding for Semi-supervised Learningcited by 232
  33. 2020ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learningcited by 209
  34. 2019iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Imagescited by 195
  35. 2021ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentationcited by 184
  36. 2020FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understandingcited by 181
  37. 2020PseudoSeg: Designing Pseudo Labels for Semantic Segmentationcited by 180
  38. 2022Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentationcited by 177

50 most cited of 130 All 130 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 6.

12 papers.

  1. 2025Pre-Trained AI Model Assisted Online Decision-Making under Missing Covariates: A Theoretical Perspectivecited by 3
  2. 2024AdaSemiCD: An Adaptive Semi-Supervised Change Detection Method Based on Pseudo-Label Evaluationcited by 2
  3. 2025Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentationcited by 1
  4. 2026FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentationcited by 1
  5. 2026Vision-Language Model Purified Semi-Supervised Semantic Segmentation for Remote Sensing Imagescited by 1
  6. 2026Fully Differentiable Bidirectional Dual-Task Synergistic Learning for Semi-Supervised 3D Medical Image Segmentationcited by 1
  7. 2025Bidirectional Uncertainty-Aware Region Learning for Semi-Supervised Medical Image Segmentationcited by 0
  8. 2025Multi-Prototype Embedding Refinement for Semi-Supervised Medical Image Segmentationcited by 0
  9. 2025OMUDA: Omni-level Masking for Unsupervised Domain Adaptation in Semantic Segmentationcited by 0
  10. 2026Addressing data annotation scarcity in Brain Tumor Segmentation on 3D MRI scan Using a Semi-Supervised Teacher-Student Frameworkcited by 0
  11. 2026Label-Efficient Learning for Ground-Based Sky-Image Classification: A Benchmark of Transfer Learning, Active Learning, and Pseudo-Labeling on GCDcited by 0
  12. 2026TC-ADA: One-Shot Active Domain Adaptation for Semantic Segmentationcited by 0