Paper Abstract and Keywords |
Presentation |
2020-05-29 14:30
A method for analyze causes of deterioration of predict quality when Deep Learning is applied to instance segmentation Tomonori Kubota, Takanori Nakao, Masafumi Katoh, Eiji Yoshida, Hidenobu Miyoshi (Fujitsu Lab.) SIP2020-14 BioX2020-14 IE2020-14 MI2020-14 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this paper, we propose a method to analyze the cause of deterioration of prediction accuracy in instance segmentation by deep learning. We have proposed a method to analyze the cause of deterioration of prediction accuracy in object recognition and object detection. This method is extended to instance segmentation (Mask Scoring R-CNN). This method extracts and visualizes the cause at the pixel grain size in the image (input image) in which the quality of the prediction result deteriorates. And, by applying the cause information of the pixel grain size extracted by this technique to the input image, it can be corrected to the image with improved prediction accuracy. That is, it can be shown that the cause information extracted by this method correctly represents the cause. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
deep learning / convolutional neural network / video analysis / segmentation / XAI / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 39, IE2020-14, pp. 67-72, May 2020. |
Paper # |
IE2020-14 |
Date of Issue |
2020-05-21 (SIP, BioX, IE, MI) |
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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SIP2020-14 BioX2020-14 IE2020-14 MI2020-14 |
Conference Information |
Committee |
MI IE SIP BioX ITE-IST ITE-ME |
Conference Date |
2020-05-28 - 2020-05-29 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Image and signal processing/analysis/AI technology, and their application |
Paper Information |
Registration To |
IE |
Conference Code |
2020-05-MI-IE-SIP-BioX-IST-ME |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A method for analyze causes of deterioration of predict quality when Deep Learning is applied to instance segmentation |
Sub Title (in English) |
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Keyword(1) |
deep learning |
Keyword(2) |
convolutional neural network |
Keyword(3) |
video analysis |
Keyword(4) |
segmentation |
Keyword(5) |
XAI |
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1st Author's Name |
Tomonori Kubota |
1st Author's Affiliation |
Fujitsu Laboratories LTD. (Fujitsu Lab.) |
2nd Author's Name |
Takanori Nakao |
2nd Author's Affiliation |
Fujitsu Laboratories LTD. (Fujitsu Lab.) |
3rd Author's Name |
Masafumi Katoh |
3rd Author's Affiliation |
Fujitsu Laboratories LTD. (Fujitsu Lab.) |
4th Author's Name |
Eiji Yoshida |
4th Author's Affiliation |
Fujitsu Laboratories LTD. (Fujitsu Lab.) |
5th Author's Name |
Hidenobu Miyoshi |
5th Author's Affiliation |
Fujitsu Laboratories LTD. (Fujitsu Lab.) |
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Speaker |
Author-1 |
Date Time |
2020-05-29 14:30:00 |
Presentation Time |
20 minutes |
Registration for |
IE |
Paper # |
SIP2020-14, BioX2020-14, IE2020-14, MI2020-14 |
Volume (vol) |
vol.120 |
Number (no) |
no.38(SIP), no.37(BioX), no.39(IE), no.40(MI) |
Page |
pp.67-72 |
#Pages |
6 |
Date of Issue |
2020-05-21 (SIP, BioX, IE, MI) |
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