  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.
