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.

翻译：语义分割是计算机视觉中最具挑战性的任务之一。然而，在许多应用中，由于像素级标注成本高昂，标注图像的匮乏成为常见障碍。在此情境下，从半监督视角出发利用有标注与无标注图像来解决问题具有合理性。近年来，这一研究方向引起了广泛关注，相关方法亦大量涌现。因此，本研究的主要目标在于综述半监督语义分割领域的最新进展，并提供涵盖现有所有方法的更新分类体系。此外，本文对文献中最广泛使用的基准数据集上代表各分类类别的多种模型进行了实验验证，并最终就所得结果、当前挑战以及最具前景的未来研究方向展开了讨论。