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Unconstrained Multimodal Multi-Label Learning
Huang, Yan1; Wang, Wei1; Wang, Liang1,2
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
2015-11-01
Volume17Issue:11Pages:1923-1935
SubtypeArticle
AbstractMultimodal learning has been mostly studied by assuming that multiple label assignments are independent of each other and all the modalities are available. In this paper, we consider a more general problem where the labels contain dependency relationships and some modalities are likely to be missing. To this end, we propose a multi-label conditional restricted Boltzmann machine (ML-CRBM), which handles modality completion, fusion, and multi-label prediction in a unified framework. The proposed model is able to generate missing modalities based on observed ones, by explicitly modelling and sampling their conditional distributions. After that, it can discriminatively fuse multiple modalities to obtain shared representations under the supervision of class labels. To consider the co-occurrence of the labels, the proposed model formulates the multi-label prediction as a max-margin-based multi-task learning problem. Model parameters can be jointly learned by seeking a balance between being generative for modality generation and being discriminative for label prediction. We perform a series of experiments in terms of classification, visualization, and retrieval, and the experimental results clearly demonstrate the effectiveness of our method.
KeywordMulti-label Learning Multi-task Learning Multimodal Learning Restricted Boltzmann Machine
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TMM.2015.2476658
WOS KeywordNEURAL-NETWORKS ; REPRESENTATION ; COLOR
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61175003 ; National Basic Research Program of China(2012CB316300) ; 61202328 ; 61572504 ; 61420106015)
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000364102400006
Citation statistics
Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10497
Collection智能感知与计算研究中心
Affiliation1.Chinese Acad Sci CASIA, Inst Automat, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China
2.CASIA, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Huang, Yan,Wang, Wei,Wang, Liang. Unconstrained Multimodal Multi-Label Learning[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2015,17(11):1923-1935.
APA Huang, Yan,Wang, Wei,&Wang, Liang.(2015).Unconstrained Multimodal Multi-Label Learning.IEEE TRANSACTIONS ON MULTIMEDIA,17(11),1923-1935.
MLA Huang, Yan,et al."Unconstrained Multimodal Multi-Label Learning".IEEE TRANSACTIONS ON MULTIMEDIA 17.11(2015):1923-1935.
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