  The aim of this paper is to give an overview of the recent advancements in
the Unsupervised Domain Adaptation (UDA) of deep networks for semantic
segmentation. This task is attracting a wide interest, since semantic
segmentation models require a huge amount of labeled data and the lack of data
fitting specific requirements is the main limitation in the deployment of these
techniques. This problem has been recently explored and has rapidly grown with
a large number of ad-hoc approaches. This motivates us to build a comprehensive
overview of the proposed methodologies and to provide a clear categorization.
In this paper, we start by introducing the problem, its formulation and the
various scenarios that can be considered. Then, we introduce the different
levels at which adaptation strategies may be applied: namely, at the input
(image) level, at the internal features representation and at the output level.
Furthermore, we present a detailed overview of the literature in the field,
dividing previous methods based on the following (non mutually exclusive)
categories: adversarial learning, generative-based, analysis of the classifier
discrepancies, self-teaching, entropy minimization, curriculum learning and
multi-task learning. Novel research directions are also briefly introduced to
give a hint of interesting open problems in the field. Finally, a comparison of
the performance of the various methods in the widely used autonomous driving
scenario is presented.
