Paper Abstract and Keywords |
Presentation |
2017-06-24 13:00
Reinforcement learning for visual attention with scalable size of attentional field Yutaro Murata, Jun-nosuke Teramae, Naoki Wakamiya (Osaka Univ.) NC2017-18 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Humans percept visual scenes with focusing only on significant parts of visual scenes sequentially rather
process whole of the visual stimuli entirely at once. Sequential visual attention attracts much attention recently in the
field of computer vision since it may largely reduce computational cost of image processing. A previous study proposed
the hard attention model, in which neural networks are trained to select suitable attended areas of visual fields for
visual recognition by using reinforcement learning. The model, however, cannot properly select attended area if sizes
of significant areas in visual scenes are different from that of training data since it cannot modulate size of attended
areas. To solve the problem, here, we propose a network model that can learn both position and size of attended area
of visual field. Since phase space features are significantly different between position and size of a selected visual scene,
they contribute to reinforcement signals in different manner, which causes undesirable bias and failure of learning. To
reduce the bias, we propose a new learning algorithm that almost keep balance of their contribution. We show that
our model can learn to suitably modulate the size of attended area and visual recognition with sequential attention. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
reinforcement learning / visual attention / image recognition / recurrent neural network / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 109, NC2017-18, pp. 121-125, June 2017. |
Paper # |
NC2017-18 |
Date of Issue |
2017-06-16 (NC) |
ISSN |
Print edition: ISSN 0913-5685 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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NC2017-18 |
Conference Information |
Committee |
NC IPSJ-BIO IBISML IPSJ-MPS |
Conference Date |
2017-06-23 - 2017-06-25 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Okinawa Institute of Science and Technology |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Machine Learning Approach to Biodata Mining, and General |
Paper Information |
Registration To |
NC |
Conference Code |
2017-06-NC-BIO-IBISML-MPS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Reinforcement learning for visual attention with scalable size of attentional field |
Sub Title (in English) |
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Keyword(1) |
reinforcement learning |
Keyword(2) |
visual attention |
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image recognition |
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recurrent neural network |
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1st Author's Name |
Yutaro Murata |
1st Author's Affiliation |
Osaka University (Osaka Univ.) |
2nd Author's Name |
Jun-nosuke Teramae |
2nd Author's Affiliation |
Osaka University (Osaka Univ.) |
3rd Author's Name |
Naoki Wakamiya |
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Osaka University (Osaka Univ.) |
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Speaker |
Author-1 |
Date Time |
2017-06-24 13:00:00 |
Presentation Time |
25 minutes |
Registration for |
NC |
Paper # |
NC2017-18 |
Volume (vol) |
vol.117 |
Number (no) |
no.109 |
Page |
pp.121-125 |
#Pages |
5 |
Date of Issue |
2017-06-16 (NC) |
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