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Paper Abstract and Keywords
Presentation 2022-03-11 14:15
Precise Affordance Annotation for Egocentric Action Video Datasets
Zecheng Yu, Yifei Huang, Ryosuke Furuta, Yusuke Goutsu, Yoichi Sato (Univ. of Tokyo) PRMU2021-81
Abstract (in Japanese) (See Japanese page) 
(in English) Object affordance has attracted a growing interest in computer vision. It is an important concept that builds a bridge between human ability and object property, and provides fine-grained information for other tasks like activity forecasting, scene understanding, etc. Although affordance is investigated in many previous works, existing affordance datasets failed to distinguish affordance from other concepts like action and function. In this paper, We propose an efficient affordance annotation scheme for egocentric action video datasets to address this issue, which gives a clear and accurate definition of affordance. The scheme was applied to two large-scale egocentric video datasets: EPIC-KITCHENS and HOMAGE, and tested with various benchmark tasks.
Keyword (in Japanese) (See Japanese page) 
(in English) affordance / egocentric / dataset / annotation / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 427, PRMU2021-81, pp. 133-138, March 2022.
Paper # PRMU2021-81 
Date of Issue 2022-03-03 (PRMU) 
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 PRMU IPSJ-CVIM  
Conference Date 2022-03-10 - 2022-03-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Differentiable rendering 
Paper Information
Registration To PRMU 
Conference Code 2022-03-PRMU-CVIM 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Precise Affordance Annotation for Egocentric Action Video Datasets 
Sub Title (in English)  
Keyword(1) affordance  
Keyword(2) egocentric  
Keyword(3) dataset  
Keyword(4) annotation  
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1st Author's Name Zecheng Yu  
1st Author's Affiliation University of Tokyo (Univ. of Tokyo)
2nd Author's Name Yifei Huang  
2nd Author's Affiliation University of Tokyo (Univ. of Tokyo)
3rd Author's Name Ryosuke Furuta  
3rd Author's Affiliation University of Tokyo (Univ. of Tokyo)
4th Author's Name Yusuke Goutsu  
4th Author's Affiliation University of Tokyo (Univ. of Tokyo)
5th Author's Name Yoichi Sato  
5th Author's Affiliation University of Tokyo (Univ. of Tokyo)
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Speaker Author-1 
Date Time 2022-03-11 14:15:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2021-81 
Volume (vol) vol.121 
Number (no) no.427 
Page pp.133-138 
#Pages
Date of Issue 2022-03-03 (PRMU) 


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