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

Source: [Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey](<https://www.sota2.com/research/paper/68fa77761e209131cea734aa>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:18:49.615Z

Authors: Lingyan Ran, Yali Li, Guoqiang Liang, Yanning Zhang

Published: 2024-03-04T10:18:38.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.01909>)

[PDF](<https://arxiv.org/pdf/2403.01909.pdf>)