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Toward few-shot domain adaptation with perturbation-invariant representation and transferable prototypes
Fan, Junsong1,2; Wang, Yuxi1,2,3; Guan, He1,2; Song, Chunfeng1,2; Zhang, Zhaoxiang1,2,3
Source PublicationFRONTIERS OF COMPUTER SCIENCE
ISSN2095-2228
2022-04
Volume16Issue:3Pages:11
Abstract

Domain adaptation (DA) for semantic segmentation aims to reduce the annotation burden for the dense pixel-level prediction task. It focuses on tackling the domain gap problem and manages to transfer knowledge learned from abundant source data to new target scenes. Although recent works have achieved rapid progress in this field, they still underperform fully supervised models with a large margin due to the absence of any available hints in the target domain. Considering that few-shot labels are cheap to obtain in practical applications, we attempt to leverage them to mitigate the performance gap between DA and fully supervised methods. The key to this problem is to leverage the few-shot labels to learn robust domain-invariant predictions effectively. To this end, we first design a data perturbation strategy to enhance the robustness of the representations. Furthermore, a transferable prototype module is proposed to bridge the domain gap based on the source data and few-shot targets. By means of these proposed methods, our approach can perform on par with the fully supervised models to some extent. We conduct extensive experiments to demonstrate the effectiveness of the proposed methods and report the state-of-the-art performance on two popular DA tasks, i.e., from GTA5 to Cityscapes and SYNTHIA to Cityscapes.

Keyworddomain adaptation semantic segmentation
DOI10.1007/s11704-022-2015-7
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2019QY1604] ; Major Project for New Generation of AI[2018AAA0100400] ; National Youth Talent Support Program ; National Natural Science Foundation of China[U21B2042] ; National Natural Science Foundation of China[62006231] ; National Natural Science Foundation of China[62072457]
Funding OrganizationNational Key R&D Program of China ; Major Project for New Generation of AI ; National Youth Talent Support Program ; National Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS IDWOS:000789054200001
PublisherHIGHER EDUCATION PRESS
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48420
Collection智能感知与计算
Corresponding AuthorZhang, Zhaoxiang
Affiliation1.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.HKISI CAS, Ctr Artificial Intelligence & Robot, Hong Kong 999077, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Fan, Junsong,Wang, Yuxi,Guan, He,et al. Toward few-shot domain adaptation with perturbation-invariant representation and transferable prototypes[J]. FRONTIERS OF COMPUTER SCIENCE,2022,16(3):11.
APA Fan, Junsong,Wang, Yuxi,Guan, He,Song, Chunfeng,&Zhang, Zhaoxiang.(2022).Toward few-shot domain adaptation with perturbation-invariant representation and transferable prototypes.FRONTIERS OF COMPUTER SCIENCE,16(3),11.
MLA Fan, Junsong,et al."Toward few-shot domain adaptation with perturbation-invariant representation and transferable prototypes".FRONTIERS OF COMPUTER SCIENCE 16.3(2022):11.
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