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Paper Abstract and Keywords
Presentation 2022-02-22 13:15
Noise-Resistant Learning for Object Detection
Jiafeng Mao, Qing Yu, Yoko Yamakata, Kiyoharu Aizawa (UTokyo)
Abstract (in Japanese) (See Japanese page) 
(in English) Supervised training of object detectors requires well-annotated large-scale datasets, whose production is extremely expensive. Therefore, some efforts have been made to obtain annotations in economical ways such as cloud sourcing. However, datasets obtained by these methods tend to contain noisy annotations such as inaccurate bounding boxes and incorrect class labels. Our research thus focuses on training object detectors on datasets with entangled classification noise and localization annotation noise. In this study, we propose a framework to distinguish and correct the noisy annotations and subsequently train the detector using the corrected annotations. We verified the effectiveness of our proposed method and compared it with state-of-the-art methods on noisy datasets with different noise levels. The experimental results show that our proposed method significantly outperforms state-of-the-art methods.
Keyword (in Japanese) (See Japanese page) 
(in English) noise-resistant / robust learning / object detection / annotation refinement / / / /  
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Conference Information
Committee AIT ME MMS IEICE-IE IEICE-ITS  
Conference Date 2022-02-21 - 2022-02-22 
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Paper Information
Registration To IEICE-IE 
Conference Code 2022-02-IE-ITS-AIT-ME-MMS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Noise-Resistant Learning for Object Detection 
Sub Title (in English)  
Keyword(1) noise-resistant  
Keyword(2) robust learning  
Keyword(3) object detection  
Keyword(4) annotation refinement  
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1st Author's Name Jiafeng Mao  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Qing Yu  
2nd Author's Affiliation The University of Tokyo (UTokyo)
3rd Author's Name Yoko Yamakata  
3rd Author's Affiliation The University of Tokyo (UTokyo)
4th Author's Name Kiyoharu Aizawa  
4th Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2022-02-22 13:15:00 
Presentation Time 15 minutes 
Registration for IEICE-IE 
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Volume (vol) vol.46 
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