Pseudo Labeling Methods for Semi-Supervised Semantic Segmentation: A Review and Future Perspectives

Semantic segmentation is a fundamental task in computer vision and finds extensive applications in scene understanding, medical image analysis, and remote sensing. With the advent of deep learning, significant advancements have been made in segmentation tasks. However, deep learning models require a substantial amount of labeled data for training, and accurately annotating datasets is labor-intensive and costly. Recently, numerous studies have explored the semantic segmentation task through the lens of semi-supervised learning, with the pseudo-labeling (PL) method emerging as a straightforward and widely applicable approach. This paper provides a comprehensive review and analysis of various PL methods and their applications in semi-supervised semantic segmentation (SSSS) from multiple angles. Initially, it captures the essence of individual model self-training and the collaborative training of multiple models from a model-centric viewpoint. Next, it explores strategies for refining or dismissing unreliable methods. Then, it categorizes techniques for addressing noisy PL data and inspects improvements in PL methods from the perspective of data augmentation. It further provides insights into optimization strategies. Furthermore, it examines PL methods from an application-oriented standpoint, such as in medical image segmentation and remote sensing image segmentation. Lastly, this paper evaluates the performance of cutting-edge methods on public datasets and concludes by discussing the challenges and potential directions for future research.