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Deep Multi-Modality Adversarial Networks for Unsupervised Domain Adaptation | |
Ma, Xinhong1,2,3; Zhang, Tianzhu1,2,3; Xu, Changsheng1,2,3 | |
发表期刊 | IEEE TRANSACTIONS ON MULTIMEDIA |
ISSN | 1520-9210 |
2019-09-01 | |
卷号 | 21期号:9页码:2419-2431 |
摘要 | Unsupervised 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. |
关键词 | Unsupervised domain adaptation triplet loss stacked attention multi-modality social event recognition |
DOI | 10.1109/TMM.2019.2902100 |
关键词[WOS] | KERNEL ; SPARSE |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Youth Innovation Promotion Association Chinese Academy of Sciences[2018166] ; Key Research Program of Frontier Sciences, Chinese Academy of Sciences[QYZDJ-SSW-JSC039] ; National Natural Science Foundation of China[61751211] ; National Natural Science Foundation of China[61728210] ; National Natural Science Foundation of China[61772244] ; Beijing Natural Science Foundation[4172062] ; National Natural Science Foundation of China[61472379] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[61572498] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61572498] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[61472379] ; Beijing Natural Science Foundation[4172062] ; National Natural Science Foundation of China[61772244] ; National Natural Science Foundation of China[61728210] ; National Natural Science Foundation of China[61751211] ; Key Research Program of Frontier Sciences, Chinese Academy of Sciences[QYZDJ-SSW-JSC039] ; Youth Innovation Promotion Association Chinese Academy of Sciences[2018166] |
WOS研究方向 | Computer Science ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
WOS记录号 | WOS:000483015200021 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 多模态智能 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/27233 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
通讯作者 | Xu, Changsheng |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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. |
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MMAN-TMM2019.pdf(2142KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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