Toward few-shot domain adaptation with perturbation-invariant representation and transferable prototypes

doi:10.1007/s11704-022-2015-7

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能感知与计算研究中心

	Toward few-shot domain adaptation with perturbation-invariant representation and transferable prototypes
	Fan, Junsong 1,2; Wang, Yuxi 1,2,3; Guan, He 1,2; Song, Chunfeng 1,2; Zhang, Zhaoxiang 1,2,3
刊名	FRONTIERS OF COMPUTER SCIENCE
	2022-06-01
卷号	16 期号:3 页码:11
关键词	domain adaptation semantic segmentation
ISSN号	2095-2228
DOI	10.1007/s11704-022-2015-7
通讯作者	Zhang, Zhaoxiang(zhaoxiang.zhang@ia.ac.cn)
英文摘要	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.
资助项目	National 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]
WOS研究方向	Computer Science
语种	英语
出版者	HIGHER EDUCATION PRESS
WOS记录号	WOS:000789054200001
资助机构	National Key R&D Program of China ; Major Project for New Generation of AI ; National Youth Talent Support Program ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48419]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Zhang, Zhaoxiang
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.HKISI CAS, Ctr Artificial Intelligence & Robot, Hong Kong 999077, Peoples R China
推荐引用方式 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.