Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey
Lingyan Ran Yali Li Guoqiang Liang Yanning Zhang
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.