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Paper Abstract and Keywords
Presentation 2024-11-30 09:30
Popularity Prediction on Fashion SNS with Multimodal Features
Shun Okamoto, Satoshi Iizuka, Kazuhiro Fukui (Tsukuba Univ.) PRMU2024-22
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, Social Networking Services (SNS) such as Facebook, X, and Flickr have developed rapidly.
Among these, fashion-related posts have gained significant attention from a marketing strategy perspective, as visual content reflects trends and eras.
However, to date, methods for predicting the popularity of SNS posts specifically focused on fashion have not been sufficiently researched.
In this study, we collect over 300,000 post data from Japan's fashion SNS ``WEAR'',
constructing a new fashion SNS dataset that includes fashion images, hashtag information, view counts, and user information.
We propose a fashion SNS popularity prediction framework that incorporates a Vision to Fashion Text Annotation (ViFTA) module, which effectively extracts semantic information from fashion posts using visual-language models.
Comparative experiments between the proposed framework and existing SNS popularity prediction methods are conducted using the new fashion SNS dataset, demonstrating that the proposed framework achieves the highest accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Fashion / SNS / New Dataset / Vision-Language Model / Multimodal Features / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 281, PRMU2024-22, pp. 77-82, Nov. 2024.
Paper # PRMU2024-22 
Date of Issue 2024-11-22 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
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)
Download PDF PRMU2024-22

Conference Information
Committee PRMU IPSJ-CVIM IPSJ-CGVI IPSJ-DCC  
Conference Date 2024-11-29 - 2024-11-30 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2024-11-PRMU-CVIM-CGVI-DCC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Popularity Prediction on Fashion SNS with Multimodal Features 
Sub Title (in English)  
Keyword(1) Fashion  
Keyword(2) SNS  
Keyword(3) New Dataset  
Keyword(4) Vision-Language Model  
Keyword(5) Multimodal Features  
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1st Author's Name Shun Okamoto  
1st Author's Affiliation University of Tsukuba (Tsukuba Univ.)
2nd Author's Name Satoshi Iizuka  
2nd Author's Affiliation University of Tsukuba (Tsukuba Univ.)
3rd Author's Name Kazuhiro Fukui  
3rd Author's Affiliation University of Tsukuba (Tsukuba Univ.)
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Speaker Author-1 
Date Time 2024-11-30 09:30:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2024-22 
Volume (vol) vol.124 
Number (no) no.281 
Page pp.77-82 
#Pages 6 
Date of Issue 2024-11-22 (PRMU) 

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