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| Presentation | 2017-02-18 15:00 Evaluation of Triple-Stream Convolutional Network for Action Recognition Dichao Liu, Yu Wang, Jien Kato, Kenji Mase (Nagoya Univ.)  PRMU2016-168 CNR2016-35 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recently, Two-Stream Convolutional Network has achieved remarkable performance. For example, by using static frames and optical flows as inputs and training the fused networks together, local motion information can be explicitly captured, bringing about noticeable improvement for end-to-end performance. On the other hand, dynamic image, whose pixel values are rescaled from parameters of a ranking machine encoding the frames’ temporal variety, have also been confirmed to provide complimentary information to spatial appearance. Inspired by these works, we proposed Triple-Stream Convolutional Network by adding a third stream of network whose input is dynamic image. In this paper, we implemented the proposed Triple-Stream Convolutional Network, and evaluated it in two aspects: (1) how the overall end-to-end classification performance can be benefited by adding the third stream; (2) which way is efficient to use the trained Triple-Stream Convolutional Network in classification. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Action recognition / ConvNets / Deep Learning / Fusion / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 461, PRMU2016-168, pp. 91-94, Feb. 2017. | |
| Paper # | PRMU2016-168 | |
| Date of Issue | 2017-02-11 (PRMU, CNR) | |
| 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) | |
| Download PDF | PRMU2016-168 CNR2016-35 |
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| Conference Information | |
| Committee | PRMU CNR |
| Conference Date | 2017-02-18 - 2017-02-19 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2017-02-PRMU-CNR |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Evaluation of Triple-Stream Convolutional Network for Action Recognition |
| Sub Title (in English) | |
| Keyword(1) | Action recognition |
| Keyword(2) | ConvNets |
| Keyword(3) | Deep Learning |
| Keyword(4) | Fusion |
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| 1st Author's Name | Dichao Liu |
| 1st Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 2nd Author's Name | Yu Wang |
| 2nd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 3rd Author's Name | Jien Kato |
| 3rd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 4th Author's Name | Kenji Mase |
| 4th Author's Affiliation | Nagoya University (Nagoya Univ.) |
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| Speaker | Author-1 |
| Date Time | 2017-02-18 15:00:00 |
| Presentation Time | 25 minutes |
| Registration for | PRMU |
| Paper # | PRMU2016-168, CNR2016-35 |
| Volume (vol) | vol.116 |
| Number (no) | no.461(PRMU), no.462(CNR) |
| Page | pp.91-94 |
| #Pages | 4 |
| Date of Issue | 2017-02-11 (PRMU, CNR) |
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