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Paper Abstract and Keywords
Presentation 2022-03-11 17:55
A study on multi-task learning toward attractiveness estimation for food photography
Mitsuaki Miyazaki, Keisuke Doman (Chukyo Univ.), Ichiro Ide (Nagoya Univ.), Yoshito Mekada (Chukyo Univ.) IMQ2021-68 IE2021-130 MVE2021-97
Abstract (in Japanese) (See Japanese page) 
(in English) We report the results of a study on multi-task learning for the accuracy improvement of attractiveness estimation of food photography. We have been studying a way of improving the estimation accuracy by effectively using a small-scale image dataset. This paper proposes another approach based on multi-task learning for attractiveness estimation. Multi-task learning is a method that simultaneously learns multiple tasks within one model. Solving a task of interest together with its related tasks can improve the generalization performance of a trained model compared to solving each task independently. Thus, we expect that such a multi-task approach is effective for the attractiveness estimation of food photography. This report proposes a multi-task learning method that simultaneously solves multiple problems including attractiveness estimation as the main task and related estimation/classification problems as subtasks, and also confirm its effectiveness through evaluation experiments.
Keyword (in Japanese) (See Japanese page) 
(in English) Food photography / attractiveness estimation / multi-task learning / regression analysis / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 423, MVE2021-97, pp. 306-311, March 2022.
Paper # MVE2021-97 
Date of Issue 2022-03-02 (IMQ, IE, MVE) 
ISSN Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee CQ IMQ MVE IE  
Conference Date 2022-03-09 - 2022-03-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Online (Zoom) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Media of five senses, Multimedia, Media experience, Picture codinge, Image media quality, Network,quality and reliability, etc 
Paper Information
Registration To MVE 
Conference Code 2022-03-CQ-IMQ-MVE-IE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study on multi-task learning toward attractiveness estimation for food photography 
Sub Title (in English)  
Keyword(1) Food photography  
Keyword(2) attractiveness estimation  
Keyword(3) multi-task learning  
Keyword(4) regression analysis  
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1st Author's Name Mitsuaki Miyazaki  
1st Author's Affiliation Chukyo University (Chukyo Univ.)
2nd Author's Name Keisuke Doman  
2nd Author's Affiliation Chukyo University (Chukyo Univ.)
3rd Author's Name Ichiro Ide  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
4th Author's Name Yoshito Mekada  
4th Author's Affiliation Chukyo University (Chukyo Univ.)
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Speaker Author-1 
Date Time 2022-03-11 17:55:00 
Presentation Time 25 minutes 
Registration for MVE 
Paper # IMQ2021-68, IE2021-130, MVE2021-97 
Volume (vol) vol.121 
Number (no) no.420(IMQ), no.422(IE), no.423(MVE) 
Page pp.306-311 
#Pages
Date of Issue 2022-03-02 (IMQ, IE, MVE) 


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