# A Survey on Semi-Supervised Semantic Segmentation Source: [A Survey on Semi-Supervised Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:01:51.507Z Authors: Adrian Pel\\'aez-Vegas, Pablo Mesejo, Juli\\'an Luengo Published: 2023-02-20T10:54:03.000Z ## Abstract 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. ## 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]() [PDF]()