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Paper Abstract and Keywords
Presentation 2020-12-17 15:10
Hierarchical Contrastive Adaptation for Cross-Domain Object Detection
Ziwei Deng, Quan Kong, Naoto Akira, Tomoaki Yoshinaga (Hitachi) PRMU2020-46
Abstract (in Japanese) (See Japanese page) 
(in English) Object detection based on deep learning has been enormously developed in recent years. However, applying detectors trained on a label-rich domain to an unseen domain results in performance drop due to the domain-shift. To deal with this problem, we propose a novel unsupervised domain adaptation framework to adapt the detector from a labeled source domain to an unlabeled target domain. In our proposed method, we utilize the image translation to generate interpolated images of source and target domains to fill in the large domain gap and facilitate a paired adaptation. We propose a hierarchical contrastive adaptation method between the original and interpolated domains to encourage the detectors to learn domain-invariant but discriminative features. To tackle the noises brought by image translation, we further propose a foreground attention reweighting for instance-aware adaptation. Experiments are carried out on 3 different scenarios of cross-domain detection and we achieve the state-of-the-art results against other approaches, showing the effectiveness of our proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Unsupervised domain adaptation / Transfer learning / Object detection / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 300, PRMU2020-46, pp. 47-52, Dec. 2020.
Paper # PRMU2020-46 
Date of Issue 2020-12-10 (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 PRMU2020-46

Conference Information
Committee PRMU  
Conference Date 2020-12-17 - 2020-12-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Transfer learning and few shot learning 
Paper Information
Registration To PRMU 
Conference Code 2020-12-PRMU 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Hierarchical Contrastive Adaptation for Cross-Domain Object Detection 
Sub Title (in English)  
Keyword(1) Unsupervised domain adaptation  
Keyword(2) Transfer learning  
Keyword(3) Object detection  
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1st Author's Name Ziwei Deng  
1st Author's Affiliation Lumada Data Science Lab. Hitachi, Ltd. (Hitachi)
2nd Author's Name Quan Kong  
2nd Author's Affiliation Lumada Data Science Lab. Hitachi, Ltd. (Hitachi)
3rd Author's Name Naoto Akira  
3rd Author's Affiliation Lumada Data Science Lab. Hitachi, Ltd. (Hitachi)
4th Author's Name Tomoaki Yoshinaga  
4th Author's Affiliation Lumada Data Science Lab. Hitachi, Ltd. (Hitachi)
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Speaker Author-1 
Date Time 2020-12-17 15:10:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2020-46 
Volume (vol) vol.120 
Number (no) no.300 
Page pp.47-52 
#Pages 6 
Date of Issue 2020-12-10 (PRMU) 

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