  Semantic image segmentation (SiS) plays a fundamental role in a broad variety
of computer vision applications, providing key information for the global
understanding of an image. This survey is an effort to summarize two decades of
research in the field of SiS, where we propose a literature review of solutions
starting from early historical methods followed by an overview of more recent
deep learning methods including the latest trend of using transformers. We
complement the review by discussing particular cases of the weak supervision
and side machine learning techniques that can be used to improve the semantic
segmentation such as curriculum, incremental or self-supervised learning.
  State-of-the-art SiS models rely on a large amount of annotated samples,
which are more expensive to obtain than labels for tasks such as image
classification. Since unlabeled data is instead significantly cheaper to
obtain, it is not surprising that Unsupervised Domain Adaptation (UDA) reached
a broad success within the semantic segmentation community. Therefore, a second
core contribution of this book is to summarize five years of a rapidly growing
field, Domain Adaptation for Semantic Image Segmentation (DASiS) which embraces
the importance of semantic segmentation itself and a critical need of adapting
segmentation models to new environments. In addition to providing a
comprehensive survey on DASiS techniques, we unveil also newer trends such as
multi-domain learning, domain generalization, domain incremental learning,
test-time adaptation and source-free domain adaptation. Finally, we conclude
this survey by describing datasets and benchmarks most widely used in SiS and
DASiS and briefly discuss related tasks such as instance and panoptic image
segmentation, as well as applications such as medical image segmentation.
