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Paper Abstract and Keywords
Presentation 2026-09-03 14:15
[Invited Lecture] Mitigating Appearance Bias in Vision-Language Models for Zero-Shot Distracted Driver Detection
Takamichi Miyata (Chita Tech) CS2026-31
Abstract (in Japanese) (See Japanese page) 
(in English) Vision-language models (VLMs) pretrained on large-scale image–text pairs enable zero-shot image recognition without task-specific additional training. However, when applying VLMs to Distracted Driver Detection, two major challenges arise: VLMs respond not only to driving behaviors but also to driver-specific appearance attributes, such as clothing, age, and gender, and the angles between text embedding vectors corresponding to different classes tend to be small. This talk focuses on mitigating bias arising from driver appearance. Specifically, we present a method that suppresses driver-specific appearance components in image embeddings using mean features estimated from multiple images of each driver. We further introduce a text embedding orthogonalization method to improve the separability of semantically similar behavior classes. Experiments on public Distracted Driver Detection datasets demonstrate consistent performance improvements over conventional zero-shot approaches. These results demonstrate the effectiveness of explicitly decoupling driver appearance from driving behavior.
Keyword (in Japanese) (See Japanese page) 
(in English) Vision-Language Models / Distracted Driver Detection / Zero-Shot Learning / Appearance Bias / Feature Decoupling / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 166, CS2026-31, pp. 17-20, Sept. 2026.
Paper # CS2026-31 
Date of Issue 2026-08-27 (CS) 
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 CS2026-31

Conference Information
Committee NS IN CS NV  
Conference Date 2026-09-03 - 2026-09-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Tohoku University + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Session management (SIP/IMS), Interoperability/Standardization, NGN/NwGN/Future networks, Cloud/Data center networks, SDN (OpenFlow, etc.)/NFV, IPv6, Machine learning, etc. 
Paper Information
Registration To CS 
Conference Code 2026-09-NS-IN-CS-NV 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Mitigating Appearance Bias in Vision-Language Models for Zero-Shot Distracted Driver Detection 
Sub Title (in English)  
Keyword(1) Vision-Language Models  
Keyword(2) Distracted Driver Detection  
Keyword(3) Zero-Shot Learning  
Keyword(4) Appearance Bias  
Keyword(5) Feature Decoupling  
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1st Author's Name Takamichi Miyata  
1st Author's Affiliation Chiba Institute of Technology (Chita Tech)
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Speaker Author-1 
Date Time 2026-09-03 14:15:00 
Presentation Time 30 minutes 
Registration for CS 
Paper # CS2026-31 
Volume (vol) vol.126 
Number (no) no.166 
Page pp.17-20 
#Pages 4 
Date of Issue 2026-08-27 (CS) 

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