Presentation 2020-03-16
Continuous Variables Estimation Through Classification Networks Ensembles
Qianyuan Liu, Yu Wang, Jien Kato,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) With the development of deep learning, CNNs have shown outstanding performance on various tasks. Previous approaches find that CNNs perform better on classification tasks than regression tasks, because regression task is a highly challenging task that approximates a mapping function from input variables to a continuous output variable. In the computer vision and mul-timedia communities, researchers address continuous variables estimation by deep convolutional neural regression networks. In this paper we make estimation of the continuous attributes of images by using classification networks ensembling. To the best of our knowledge, this is the first attempt to address regression problems through classification networks ensembles and our proposed method shows great versatility in different datasets. Experiment results show that our proposed method outper-forms the regression method and single classification network.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Regression taskClassification taskNetworks EnsemblingCNNs
Paper # PRMU2019-87
Date of Issue 2020-03-09 (PRMU)

Conference Information
Committee PRMU / IPSJ-CVIM
Conference Date 2020/3/16(2days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Yoichi Sato(Univ. of Tokyo)
Vice Chair Toru Tamaki(Hiroshima Univ.) / Akisato Kimura(NTT)
Secretary Toru Tamaki(NTT) / Akisato Kimura(OMRON SINICX)
Assistant Yusuke Uchida(DeNA) / Takayoshi Yamashita(Chubu Univ.)

Paper Information
Registration To Technical Committee on Pattern Recognition and Media Understanding / Special Interest Group on Computer Vision and Image Media
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Continuous Variables Estimation Through Classification Networks Ensembles
Sub Title (in English)
Keyword(1) Regression taskClassification taskNetworks EnsemblingCNNs
1st Author's Name Qianyuan Liu
1st Author's Affiliation Nagoya University(Nagoya Univ.)
2nd Author's Name Yu Wang
2nd Author's Affiliation Ritsumeikan University(Ritsumeikan Univ.)
3rd Author's Name Jien Kato
3rd Author's Affiliation Ritsumeikan University(Ritsumeikan Univ.)
Date 2020-03-16
Paper # PRMU2019-87
Volume (vol) vol.119
Number (no) PRMU-481
Page pp.pp.109-114(PRMU),
#Pages 6
Date of Issue 2020-03-09 (PRMU)