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Paper Abstract and Keywords
Presentation 2025-12-16 11:00
Deep learning models trained on stereo natural images develop neural-like representations of object size
Hiroto Yonekawa, Takahisa M. Sanada (Iwate Prefectural Univ.) HIP2025-73
Abstract (in Japanese) (See Japanese page) 
(in English) We perceive an object’s size as constant even when its retinal image size varies with viewing distance. Deep learning models achieve high object-recognition performance and share several properties with human visual system. In this study, we investigated whether deep learning model trained on stereo natural images exhibit size constancy. When the model was retrained to perform size discrimination, it exhibited depth-dependent size judgments similar to those observed in humans. Furthermore, in the higher layers of the model, we identified units whose response amplitudes were modulated by viewing depth even for stimuli of identical physical size, exhibiting a pattern that correlates with size constancy.
Keyword (in Japanese) (See Japanese page) 
(in English) Size constancy / Deep learning model / Stereopsis / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 291, HIP2025-73, pp. 42-46, Dec. 2025.
Paper # HIP2025-73 
Date of Issue 2025-12-08 (HIP) 
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 HIP2025-73

Conference Information
Committee HIP  
Conference Date 2025-12-15 - 2025-12-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Research Institute of Electrical Communication 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Multi-modal, KANSEI information processing, Vision and its application, Human information processing 
Paper Information
Registration To HIP 
Conference Code 2025-12-HIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep learning models trained on stereo natural images develop neural-like representations of object size 
Sub Title (in English)  
Keyword(1) Size constancy  
Keyword(2) Deep learning model  
Keyword(3) Stereopsis  
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1st Author's Name Hiroto Yonekawa  
1st Author's Affiliation Iwate Prefectural University (Iwate Prefectural Univ.)
2nd Author's Name Takahisa M. Sanada  
2nd Author's Affiliation Iwate Prefectural University (Iwate Prefectural Univ.)
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Speaker Author-1 
Date Time 2025-12-16 11:00:00 
Presentation Time 30 minutes 
Registration for HIP 
Paper # HIP2025-73 
Volume (vol) vol.125 
Number (no) no.291 
Page pp.42-46 
#Pages 5 
Date of Issue 2025-12-08 (HIP) 

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