Paper Abstract and Keywords |
Presentation |
2020-12-18 15:25
Multi-Task Attention Learning for Fine-grained Recognition Dichao Liu (NU), Yu Wang (Rits), Kenji Mase (NU), Jien Kato (Rits) PRMU2020-63 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Due to its inter-class similarity and intra-class variation, Fine-Grained Image Classification (FGIC) is an intrinsically difficult task. Most of the current studies solve this problem by localizing important local regions and then learning region-based features. Such methods, however, still face the issue of loss of information or high computational expenses. In this work, we concentrate on reinforcing the correspondence of the deep neural network to attention regions instead of part localization. We propose a new end-to-end optimization method called Multi-Task Attention Learning (MTAL) that can be implemented with the Soft Mask (SM) module and the Hard Crop (HC) module,which are two separate types of attention-generation modules. Experimental results on CUB-Birds and Stanford Cars show that, despite its simplicity, our procedure performs better than the baselines and is comparable to state-of-the-art studies. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Fine-grained image classification / Multi-task learning / Attention learning / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 300, PRMU2020-63, pp. 145-150, Dec. 2020. |
Paper # |
PRMU2020-63 |
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) |
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PRMU2020-63 |
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) |
Multi-Task Attention Learning for Fine-grained Recognition |
Sub Title (in English) |
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Fine-grained image classification |
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Multi-task learning |
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Attention learning |
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1st Author's Name |
Dichao Liu |
1st Author's Affiliation |
Nagoya University (NU) |
2nd Author's Name |
Yu Wang |
2nd Author's Affiliation |
Ritsumeikan University (Rits) |
3rd Author's Name |
Kenji Mase |
3rd Author's Affiliation |
Nagoya University (NU) |
4th Author's Name |
Jien Kato |
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Ritsumeikan University (Rits) |
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Speaker |
Author-1 |
Date Time |
2020-12-18 15:25:00 |
Presentation Time |
15 minutes |
Registration for |
PRMU |
Paper # |
PRMU2020-63 |
Volume (vol) |
vol.120 |
Number (no) |
no.300 |
Page |
pp.145-150 |
#Pages |
6 |
Date of Issue |
2020-12-10 (PRMU) |