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Title: Scale variance minimization for unsupervised domain adaptation in image segmentation
Authors: Guan, Dayan
Huang, Jiaxing
Lu, Shijian
Xiao, Aoran
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Guan, D., Huang, J., Lu, S. & Xiao, A. (2021). Scale variance minimization for unsupervised domain adaptation in image segmentation. Pattern Recognition, 112, 107764-.
Journal: Pattern Recognition 
Abstract: We focus on unsupervised domain adaptation (UDA) in image segmentation. Existing works address this challenge largely by aligning inter-domain representations, which may lead over-alignment that impairs the semantic structures of images and further target-domain segmentation performance. We design a scale variance minimization (SVMin) method by enforcing the intra-image semantic structure consistency in the target domain. Specifically, SVMin leverages an intrinsic property that simple scale transformation has little effect on the semantic structures of images. It thus introduces certain supervision in the target domain by imposing a scale-invariance constraint while learning to segment an image and its scale-transformation concurrently. Additionally, SVMin is complementary to most existing UDA techniques and can be easily incorporated with consistent performance boost but little extra parameters. Extensive experiments show that our method achieves superior domain adaptive segmentation performance as compared with the state-of-the-art. Preliminary studies show that SVMin can be easily adapted for UDA-based image classification.
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2020.107764
Schools: School of Computer Science and Engineering 
Research Centres: Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU)
Rights: © 2020 Elsevier Ltd. All rights reserved. This paper was published in Pattern Recognition and is made available with permission of Elsevier Ltd.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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