中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Learning Feature Representation and Partial Correlation for Multimodal Multi-Label Data

文献类型：期刊论文

作者Song, Guoli4; Wang, Shuhui3; Huang, Qingming2,3,4; Tian, Qi1
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2021
卷号23页码:1882-1894
关键词Semantics Correlation Task analysis Data models Learning systems Kernel Deep learning Cross-modal retrieval correlation learning feature learning partial correlation
ISSN号1520-9210
DOI10.1109/TMM.2020.3004963
英文摘要User-provided annotations in existing multimodal datasets sometimes are inappropriate for model learning and can hinder the task of cross-modal retrieval. To handle this issue, we propose a discriminative and noise-robust cross-modal retrieval method, called FLPCL, which consists of deep feature learning and partial correlation learning. Deep feature learning is implemented by utilizing label supervised information to guide the training of deep neural network for each modality, which aims to find modality-specific deep feature representations that preserve the similarity and discrimination information among multimodal data. Based on deep feature learning, partial correlation learning is proposed to infer direct association between different modalities by removing the effect of common underlying semantics from each modality. It is achieved by maximizing the canonical correlation of the feature representations of different modalities conditioned on the label modality. Different from existing works that build indirect association between modalities via incorporating semantic labels, our FLPCL method can learn more effective and robust multimodal latent representations by explicitly preserving both intra-modal and inter-modal relationship among multimodal data. Extensive experiments on three cross-modal datasets show that our method outperforms state-of-the-art methods on cross-modal retrieval tasks.
资助项目National Key R&D Program of China[2018AAA0102003] ; National Natural Science Foundation of China[61672497] ; National Natural Science Foundation of China[61836002] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61931008] ; Key Research Program of Frontier Sciences of CAS[QYZDJ-SSW-SYS013]
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000668875100005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/17513]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Shuhui
作者单位1.Huawei, Cloud BU, Shenzhen 518129, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China
4.Peng Cheng Lab, Shenzhen, Peoples R China
推荐引用方式
GB/T 7714		 Song, Guoli,Wang, Shuhui,Huang, Qingming,et al. Learning Feature Representation and Partial Correlation for Multimodal Multi-Label Data[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:1882-1894.
APA Song, Guoli,Wang, Shuhui,Huang, Qingming,&Tian, Qi.(2021).Learning Feature Representation and Partial Correlation for Multimodal Multi-Label Data.IEEE TRANSACTIONS ON MULTIMEDIA,23,1882-1894.
MLA Song, Guoli,et al."Learning Feature Representation and Partial Correlation for Multimodal Multi-Label Data".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):1882-1894.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。