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Paper Abstract and Keywords
Presentation 2021-12-16 14:45
Unsupervised Logo Detection Using Adversarial Learning from Synthetic to Real Images
Rahul Kumar Jain (Ritsumeikan Univ.), Takahiro Sato, Taro Watasue, Tomohiro Nakagawa (tiwaki), Yutaro Iwamoto (Ritsumeikan Univ.), Xiang Ruan (tiwaki), Yen-Wei Chen (Ritsumeikan Univ.) PRMU2021-31
Abstract (in Japanese) (See Japanese page) 
(in English) Most of the existing deep learning based logo detection methods typically use a large amount of annotated training data, assuming that the training and test data belong to the same data distribution. Synthesized training images with automatically generated object-level annotations can be a solution to avoid the labor-intensive and time-consuming object annotation task. However, real-world problems limit this assumption and object detectors face domain-shift problems resulting in performance degradation. Here, we address the domain-shift problem in the field of logo detection from synthetic to real images. In this paper, to align the domain gap from synthetic to real image, we propose to use entropy minimization of mid-level output feature maps. We also synthesize training images using various data augmentation methods to perform experiments. Our experiments show that our proposed method improves performance by around 4% mAP compared to direct transfer from source to target domain (synthetic-to-real images) without any labeling cost and increasing network parameters.
Keyword (in Japanese) (See Japanese page) 
(in English) Unsupervised Domain Adaptation / Adversarial Learning / Anchorless Object Detectors / Entropy Minimization / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 304, PRMU2021-31, pp. 43-44, Dec. 2021.
Paper # PRMU2021-31 
Date of Issue 2021-12-09 (PRMU) 
ISSN Online edition: ISSN 2432-6380
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  
Conference Date 2021-12-16 - 2021-12-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2021-12-PRMU 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Unsupervised Logo Detection Using Adversarial Learning from Synthetic to Real Images 
Sub Title (in English)  
Keyword(1) Unsupervised Domain Adaptation  
Keyword(2) Adversarial Learning  
Keyword(3) Anchorless Object Detectors  
Keyword(4) Entropy Minimization  
1st Author's Name Rahul Kumar Jain  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Takahiro Sato  
2nd Author's Affiliation tiwaki Co. Ltd. (tiwaki)
3rd Author's Name Taro Watasue  
3rd Author's Affiliation tiwaki Co. Ltd. (tiwaki)
4th Author's Name Tomohiro Nakagawa  
4th Author's Affiliation tiwaki Co. Ltd. (tiwaki)
5th Author's Name Yutaro Iwamoto  
5th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
6th Author's Name Xiang Ruan  
6th Author's Affiliation tiwaki Co. Ltd. (tiwaki)
7th Author's Name Yen-Wei Chen  
7th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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Speaker Author-1 
Date Time 2021-12-16 14:45:00 
Presentation Time 10 minutes 
Registration for PRMU 
Paper # PRMU2021-31 
Volume (vol) vol.121 
Number (no) no.304 
Page pp.43-44 
Date of Issue 2021-12-09 (PRMU) 

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