mlsanityData to 14 Oct 2026

A Survey on Semi-Supervised Semantic Segmentation

arXiv 2302.09899 ↗First posted 20 Feb 2023cs.CVcited by 17

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Abstract (arXiv)

Semantic segmentation is one of the most challenging tasks in computer vision. However, in many applications, a frequent obstacle is the lack of labeled images, due to the high cost of pixel-level labeling. In this scenario, it makes sense to approach the problem from a semi-supervised point of view, where both labeled and unlabeled images are exploited. In recent years this line of research has gained much interest and many approaches have been published in this direction. Therefore, the main objective of this study is to provide an overview of the current state of the art in semi-supervised semantic segmentation, offering an updated taxonomy of all existing methods to date. This is complemented by an experimentation with a variety of models representing all the categories of the taxonomy on the most widely used becnhmark datasets in the literature, and a final discussion on the results obtained, the challenges and the most promising lines of future research.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 83 of its 170 references that are in the map. First posted on arXiv 20 Feb 2023.

Builds on

83 of 170 references.

References · 83

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 10; 2026: 2.

17 papers.

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  1. 2024Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Surveycited by 12
  2. 2025Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attentioncited by 10
  3. 2024Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Studycited by 5
  4. 2024Leveraging Task-Specific Knowledge from LLM for Semi-Supervised 3D Medical Image Segmentationcited by 4
  5. 2025Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentationcited by 3
  6. 2025Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustnesscited by 3
  7. 2023Semi-Supervised Semantic Segmentation of Cell Nuclei via Diffusion-based Large-Scale Pre-Training and Collaborative Learningcited by 2
  8. 2025SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineationcited by 2
  9. 2025SemiHMER: Semi-supervised Handwritten Mathematical Expression Recognition using pseudo-labelscited by 1
  10. 2025Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentationcited by 1
  11. 2025RS-MTDF: Multi-Teacher Distillation and Fusion for Remote Sensing Semi-Supervised Semantic Segmentationcited by 1
  12. 2025Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasetscited by 1
5 more
  1. 2023Three-dimensional Bone Image Synthesis with Generative Adversarial Networkscited by 0
  2. 2025Transport-Related Surface Detection with Machine Learning: Analyzing Temporal Trends in Madrid and Viennacited by 0
  3. 2025FRIEREN: Federated Learning with Vision-Language Regularization for Segmentationcited by 0
  4. 2026Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentationcited by 0
  5. 2026Label-Efficient Deep Learning for ECG Delineation: A Multi-Dataset Benchmark against Widely Used Delineation Toolscited by 0