Recent Advances in Deep Domain Adaptation Research for Semantic Segmentation in Urban Scenes

Siyu Zhu, Qitao Tai, Lingyu Du, Lin Miao (), Xiulei Liu, Ning Li and Shoulu Hou
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Siyu Zhu: Computer School, Beijing Information Science and Technology University, No. 55 Taihang Road, Changping District, Beijing 102206, China
Qitao Tai: Computer School, Beijing Information Science and Technology University, No. 55 Taihang Road, Changping District, Beijing 102206, China
Lingyu Du: Computer School, Beijing Information Science and Technology University, No. 55 Taihang Road, Changping District, Beijing 102206, China
Lin Miao: Computer School, Beijing Information Science and Technology University, No. 55 Taihang Road, Changping District, Beijing 102206, China
Xiulei Liu: Computer School, Beijing Information Science and Technology University, No. 55 Taihang Road, Changping District, Beijing 102206, China
Ning Li: Computer School, Beijing Information Science and Technology University, No. 55 Taihang Road, Changping District, Beijing 102206, China
Shoulu Hou: Computer School, Beijing Information Science and Technology University, No. 55 Taihang Road, Changping District, Beijing 102206, China

Mathematics, 2025, vol. 13, issue 22, 1-31

Abstract: Domain adaptation in image semantic segmentation has attracted more and more attention from computer vision and machine learning researchers. While the high cost of manual annotation is an unavoidable bottleneck in the semantic segmentation task, it is a high-quality solution to adopt pixel-level annotation from synthetic data, which provides additional support for deep learning training. Numerous studies have attempted to comprehensively investigate deep domain adaptation, but there is less focus on the sub-direction of the semantic segmentation task. This paper is devoted to this new topic in transfer learning. First, we describe the terminology and background concepts in this field. Next, the main datasets and evaluation metrics are introduced. Then, we classify the current research methods and introduce their contributions. Moreover, the quantitative results of the methods involved are compared, and the results are discussed. Finally, we suggest future research directions for this field. We believe researchers who are interested in this field will find this work to be an effective reference.

Keywords: domain adaptation; semantic segmentation; transfer learning; unsupervised learning; urban scene understanding (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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