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Presentation 2022-09-09 10:00
Presentation Slide Assessment System using Visual and Semantic Segmentation Features
Shengzhou Yi (UTokyo), Junichiro Matsugami (Rubato), Toshihiko Yamasaki (UTokyo) MVE2022-13
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we present a new presentation slide assessment system that can consider structural features of the slides more easily. Our previous work used a neural network to identify novice vs. well-designed presentation slides based on visual and structural features. However, the structural feature extraction was based on the bounding box information of a PPTX file. Therefore, it is unavailable for the users who are unwilling to upload editable PPTX files and those who use other applications such as Google Slides and Keynote. In order to solve this problem, we extract the semantic segmentation of presentation slides from the slide images as a new format of structural features to replace the previous structural features extracted from XML files (i.e., PPTX files). The proposed multi-modal Transformer extracts the visual and structural features from the original images and semantic segmentation results, respectively, to assess the slide design. The prediction targets are the top-10 checkpoints pointed out by the professional consultants. Class-imbalanced learning methods are used for addressing the imbalanced label distribution, and multi-task learning are also applied to improve the accuracy of the proposed model. In the optimal settings of the used machine learning methods for each checkpoint, the proposed model only requiring slide images achieved an average accuracy of 81.67% that is comparative to the performance of the previous work requiring slide images and XML files.
Keyword (in Japanese) (See Japanese page) 
(in English) Presentation Slide / Feature Learning / Class Imbalance / Multi-Task Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 175, MVE2022-13, pp. 16-21, Sept. 2022.
Paper # MVE2022-13 
Date of Issue 2022-09-01 (MVE) 
ISSN Print edition: ISSN 0913-5685  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 MVE  
Conference Date 2022-09-08 - 2022-09-09 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MVE 
Conference Code 2022-09-MVE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Presentation Slide Assessment System using Visual and Semantic Segmentation Features 
Sub Title (in English)  
Keyword(1) Presentation Slide  
Keyword(2) Feature Learning  
Keyword(3) Class Imbalance  
Keyword(4) Multi-Task Learning  
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1st Author's Name Shengzhou Yi  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Junichiro Matsugami  
2nd Author's Affiliation Rubato Co., Ltd. (Rubato)
3rd Author's Name Toshihiko Yamasaki  
3rd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2022-09-09 10:00:00 
Presentation Time 30 minutes 
Registration for MVE 
Paper # MVE2022-13 
Volume (vol) vol.122 
Number (no) no.175 
Page pp.16-21 
#Pages
Date of Issue 2022-09-01 (MVE) 


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