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Open AccessReview

Unsupervised Domain Adaptation in Semantic Segmentation: A Review

Department of Information Engineering, University of Padova, 35131 Padova, Italy
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Technologies 2020, 8(2), 35; https://doi.org/10.3390/technologies8020035
Received: 22 May 2020 / Revised: 12 June 2020 / Accepted: 17 June 2020 / Published: 21 June 2020
(This article belongs to the Section Information and Communication Technologies)
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 field 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. View Full-Text
Keywords: unsupervised domain adaptation; semantic segmentation; unsupervised learning; deep learning; transfer learning; survey unsupervised domain adaptation; semantic segmentation; unsupervised learning; deep learning; transfer learning; survey
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MDPI and ACS Style

Toldo, M.; Maracani, A.; Michieli, U.; Zanuttigh, P. Unsupervised Domain Adaptation in Semantic Segmentation: A Review. Technologies 2020, 8, 35.

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