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MMT: Cross Domain Few-Shot Learning via Meta-Memory Transfer
Wang, Wenjian1,2,3; Duan, Lijuan1,2,3; Wang, Yuxi4; Fan, Junsong; Zhang, Zhaoxiang4,5,6
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
2023-12-01
Volume45Issue:12Pages:15018-15035
Corresponding AuthorZhang, Zhaoxiang(zhaoxiang.zhang@ia.ac.cn)
AbstractFew-shot learning aims to recognize novel categories solely relying on a few labeled samples, with existing few-shot methods primarily focusing on the categories sampled from the same distribution. Nevertheless, this assumption cannot always be ensured, and the actual domain shift problem significantly reduces the performance of few-shot learning. To remedy this problem, we investigate an interesting and challenging cross-domain few-shot learning task, where the training and testing tasks employ different domains. Specifically, we propose aMeta-Memory scheme to bridge the domain gap between source and target domains, leveraging style-memory and content-memory components. The former stores intra-domain style information from source domain instances and provides a richer feature distribution. The latter stores semantic information through exploration of knowledge of different categories. Under the contrastive learning strategy, our model effectively alleviates the cross-domain problem in few-shot learning. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on cross-domain few-shot semantic segmentation tasks on the COCO-20(i), PASCAL-5(i), FSS-1000, and SUIM datasets and positively affects few-shot classification tasks on Meta-Dataset.
KeywordMemory few-shot learning semantic segmentation cross-domain
DOI10.1109/TPAMI.2023.3306352
WOS KeywordNETWORKS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61836014] ; National Natural Science Foundation of China[U21B2042] ; National Natural Science Foundation of China[62072457] ; National Natural Science Foundation of China[62006231] ; National Natural Science Foundation of China[62176009] ; Major Project for New Generation of AI[2018AAA0100400]
Funding OrganizationNational Natural Science Foundation of China ; Major Project for New Generation of AI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:001130146400060
PublisherIEEE COMPUTER SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55516
Collection智能感知与计算研究中心
Corresponding AuthorZhang, Zhaoxiang
Affiliation1.Beijing Univ Technol, Fac Informat Technol, Beijing 100021, Peoples R China
2.Beijing Key Lab Trusted Comp, Beijing 100124, Peoples R China
3.Natl Engn Lab Key Technol Informat Secur Level Pr, Beijing 100124, Peoples R China
4.Chinese Acad Sci, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong 999077, Peoples R China
5.Chinese Acad Sci CASIA, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100045, Peoples R China
6.Univ Chinese Acad Sci UCAS, Beijing 101408, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Wenjian,Duan, Lijuan,Wang, Yuxi,et al. MMT: Cross Domain Few-Shot Learning via Meta-Memory Transfer[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(12):15018-15035.
APA Wang, Wenjian,Duan, Lijuan,Wang, Yuxi,Fan, Junsong,&Zhang, Zhaoxiang.(2023).MMT: Cross Domain Few-Shot Learning via Meta-Memory Transfer.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(12),15018-15035.
MLA Wang, Wenjian,et al."MMT: Cross Domain Few-Shot Learning via Meta-Memory Transfer".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.12(2023):15018-15035.
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