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DC Field | Value | Language |
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dc.contributor.author | Guan, Dayan | en_US |
dc.contributor.author | Huang, Jiaxing | en_US |
dc.contributor.author | Lu, Shijian | en_US |
dc.contributor.author | Xiao, Aoran | en_US |
dc.date.accessioned | 2022-05-01T06:34:41Z | - |
dc.date.available | 2022-05-01T06:34:41Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Guan, D., Huang, J., Lu, S. & Xiao, A. (2021). Scale variance minimization for unsupervised domain adaptation in image segmentation. Pattern Recognition, 112, 107764-. https://dx.doi.org/10.1016/j.patcog.2020.107764 | en_US |
dc.identifier.issn | 0031-3203 | en_US |
dc.identifier.uri | https://hdl.handle.net/10356/157050 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Pattern Recognition | en_US |
dc.rights | © 2020 Elsevier Ltd. All rights reserved. This paper was published in Pattern Recognition and is made available with permission of Elsevier Ltd. | en_US |
dc.subject | Engineering::Computer science and engineering | en_US |
dc.title | Scale variance minimization for unsupervised domain adaptation in image segmentation | en_US |
dc.type | Journal Article | en |
dc.contributor.school | School of Computer Science and Engineering | en_US |
dc.contributor.research | Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU) | en_US |
dc.identifier.doi | 10.1016/j.patcog.2020.107764 | - |
dc.description.version | Submitted/Accepted version | en_US |
dc.identifier.scopus | 2-s2.0-85097709318 | - |
dc.identifier.volume | 112 | en_US |
dc.identifier.spage | 107764 | en_US |
dc.subject.keywords | Unsupervised Domain Adaptation | en_US |
dc.subject.keywords | Image Segmentation | en_US |
dc.description.acknowledgement | This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund - Industry Collaboration Projects Grant. | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
Appears in Collections: | SCSE Journal Articles |
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File | Description | Size | Format | |
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Scale variance minimization for unsupervised domain adaptation in image segmentation.pdf | 25.68 MB | Adobe PDF | View/Open |
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