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Spectral attribute learning for visual regression
Chen, Ke1; Jia, Kui2; Zhang, Zhaoxiang3; Kamarainen, Joni-Kristian1; Kui Jia
AbstractA number of computer vision problems such as facial age estimation, crowd counting and pose estimation can be solved by learning regression mapping on low-level imagery features. We show that visual regression can be substantially improved by two-stage regression where imagery features are first mapped to an attribute space which explicitly models latent correlations across continuously-changing output. We propose an approach to automatically discover "spectral attributes" which avoids manual work required for defining hand-crafted attribute representations. Visual attribute regression outperforms direct visual regression and our spectral attribute visual regression achieves state-of-the-art accuracy in multiple applications.
KeywordFacial Age Estimation Crowd Counting Head Pose Estimation Spectral Learning Attributes Regression
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationAcademy of Finland(267581 ; 298700)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000397371800009
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Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorKui Jia
Affiliation1.Tampere Univ Technol, Dept Signal Proc, Tampere, Finland
2.South China Univ Technol, Sch Elect & Informat Engn, Guangzhou, Guangdong, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Chen, Ke,Jia, Kui,Zhang, Zhaoxiang,et al. Spectral attribute learning for visual regression[J]. PATTERN RECOGNITION,2017,66(0):74-81.
APA Chen, Ke,Jia, Kui,Zhang, Zhaoxiang,Kamarainen, Joni-Kristian,&Kui Jia.(2017).Spectral attribute learning for visual regression.PATTERN RECOGNITION,66(0),74-81.
MLA Chen, Ke,et al."Spectral attribute learning for visual regression".PATTERN RECOGNITION 66.0(2017):74-81.
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