CASIA OpenIR
Deep Multi-Modality Adversarial Networks for Unsupervised Domain Adaptation
Ma, Xinhong1,2,3; Zhang, Tianzhu1,2,3; Xu, Changsheng1,2,3
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
ISSN1520-9210
2019-09-01
Volume21Issue:9Pages:2419-2431
Corresponding AuthorXu, Changsheng(csxu@nlpr.ia.ac.cn)
AbstractUnsupervised domain adaptation aims to transfer domain knowledge from existing well-defined tasks to new ones where labels are unavailable. In the real-world applications, domain discrepancy is usually uncontrollable especially for multi-modality data. Therefore, it is significantly motivated to deal with a multi-modality domain adaptation task. As labels are unavailable in a target domain, how to learn semantic multi-modality representations and successfully adapt the classifier from a source to the target domain remain open challenges in a multi-modality domain adaptation task. To deal with these issues, we propose a multi-modality adversarial network (MMAN), which applies stacked attention to learn semantic multi-modality representations and reduces domain discrepancy via adversarial training. Unlike the previous domain adaptation methods, which cannot make full use of source domain categories information, multi-channel constraint is employed to capture fine-grained categories of knowledge that could enhance the discrimination of target samples and boost target performance on single-modality and multi-modality domain adaptation problems. We apply the proposed MMAN to two applications including cross-domain object recognition and cross-domain social event recognition. The extensive experimental evaluations demonstrate the effectiveness of the proposed model for unsupervised domain adaptation.
KeywordUnsupervised domain adaptation triplet loss stacked attention multi-modality social event recognition
DOI10.1109/TMM.2019.2902100
WOS KeywordKERNEL ; SPARSE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61572498] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[61728210] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61751211] ; National Natural Science Foundation of China[61772244] ; National Natural Science Foundation of China[61472379] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[U1705262] ; Key Research Program of Frontier Sciences, Chinese Academy of Sciences[QYZDJ-SSW-JSC039] ; Beijing Natural Science Foundation[4172062] ; Youth Innovation Promotion Association Chinese Academy of Sciences[2018166]
Funding OrganizationNational Natural Science Foundation of China ; Key Research Program of Frontier Sciences, Chinese Academy of Sciences ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association Chinese Academy of Sciences
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000483015200021
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27233
Collection中国科学院自动化研究所
Corresponding AuthorXu, Changsheng
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Peng Cheng Lab, Shenzhen, 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
Ma, Xinhong,Zhang, Tianzhu,Xu, Changsheng. Deep Multi-Modality Adversarial Networks for Unsupervised Domain Adaptation[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2019,21(9):2419-2431.
APA Ma, Xinhong,Zhang, Tianzhu,&Xu, Changsheng.(2019).Deep Multi-Modality Adversarial Networks for Unsupervised Domain Adaptation.IEEE TRANSACTIONS ON MULTIMEDIA,21(9),2419-2431.
MLA Ma, Xinhong,et al."Deep Multi-Modality Adversarial Networks for Unsupervised Domain Adaptation".IEEE TRANSACTIONS ON MULTIMEDIA 21.9(2019):2419-2431.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Ma, Xinhong]'s Articles
[Zhang, Tianzhu]'s Articles
[Xu, Changsheng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ma, Xinhong]'s Articles
[Zhang, Tianzhu]'s Articles
[Xu, Changsheng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ma, Xinhong]'s Articles
[Zhang, Tianzhu]'s Articles
[Xu, Changsheng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.