Paper Abstract and Keywords |
Presentation |
2022-03-11 14:30
Background Mixup Data Augmentation for Hand and Object-in-Contact Detection Koya Tango, Takehiko Ohkawa, Ryosuke Furuta, Yoichi Sato (UTokyo) PRMU2021-82 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Detecting the position of human hands and an object-in-contact from an image is vital for understanding a user’s actions in a video and logging his or her daily activities.
For training an object detector, Mixup, which overlays two training images to mitigate data bias, has been studied as an empirically powerful augmentation.
However, in our task of hand and object-in-contact detection, mixing two human activity images produces an unintended bias in the augmented training data, e.g., the concentration of hands and objects in a specific region degrades the ability of the hand and object-in-contact detector to identify object boundaries.
In this work, we propose Background Mixup data augmentation that leverages data mixing regularization while reducing the unintended effect in hand and object-in-contact detection.
Specifically, instead of mixing two images where a hand and object-in-contact pair appears, we mix a target training image with background images extracted from external image sources and use them to train the detector.
Our experimental study demonstrated that the proposed method effectively reduced false positives and improved the performance of hand and object-in-contact detection in supervised and semi-supervised learning settings. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Data Augmentation / Hand and Object-in-Contact Detection / Mixup / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 427, PRMU2021-82, pp. 139-144, March 2022. |
Paper # |
PRMU2021-82 |
Date of Issue |
2022-03-03 (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) |
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PRMU2021-82 |
Conference Information |
Committee |
PRMU IPSJ-CVIM |
Conference Date |
2022-03-10 - 2022-03-11 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Differentiable rendering |
Paper Information |
Registration To |
PRMU |
Conference Code |
2022-03-PRMU-CVIM |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Background Mixup Data Augmentation for Hand and Object-in-Contact Detection |
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Keyword(1) |
Data Augmentation |
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Hand and Object-in-Contact Detection |
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Mixup |
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1st Author's Name |
Koya Tango |
1st Author's Affiliation |
The University of Tokyo (UTokyo) |
2nd Author's Name |
Takehiko Ohkawa |
2nd Author's Affiliation |
The University of Tokyo (UTokyo) |
3rd Author's Name |
Ryosuke Furuta |
3rd Author's Affiliation |
The University of Tokyo (UTokyo) |
4th Author's Name |
Yoichi Sato |
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The University of Tokyo (UTokyo) |
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Speaker |
Author-1 |
Date Time |
2022-03-11 14:30:00 |
Presentation Time |
15 minutes |
Registration for |
PRMU |
Paper # |
PRMU2021-82 |
Volume (vol) |
vol.121 |
Number (no) |
no.427 |
Page |
pp.139-144 |
#Pages |
6 |
Date of Issue |
2022-03-03 (PRMU) |
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