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| Presentation | 2021-05-21 10:30 A Study on Domain Adaptation for Video Action Classification Utilizing Synthetic Data. Hana Isoi (Ochanomizu Univ.), Atsuko Takefusa (NII), Hidemoto Nakada (AIST), Masato Oguchi (Ochanomizu Univ.)  PRMU2021-5 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The lack of learning data is considered as one of the reasons why the classification accuracies of deep neural networks do not improve. Synthetic data are used for learning to resolve the above issue because they can be generated relatively easier than real data. However, the models trained using synthetic data generally show low classification accuracies for actual data analysis due to domain shift that is differences in data characteristics. To achieve highly accurate video action classification using synthetic video data, we create a realistic synthetic video data set and investigate the video classification accuracies using the data set. The experimental results show that the accuracies can be improved by using data augmentation methods and DNN-based domain adaptation. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | domain adaptation / synthetic data / data augmentation / adversarial learning / video classification / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 23, PRMU2021-5, pp. 25-30, May 2021. | |
| Paper # | PRMU2021-5 | |
| Date of Issue | 2021-05-13 (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) | |
| Download PDF | PRMU2021-5 |
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| Conference Information | |
| Committee | PRMU IPSJ-CVIM IPSJ-NL |
| Conference Date | 2021-05-20 - 2021-05-21 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Vision & Language |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2021-05-PRMU-CVIM-NL |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on Domain Adaptation for Video Action Classification Utilizing Synthetic Data. |
| Sub Title (in English) | |
| Keyword(1) | domain adaptation |
| Keyword(2) | synthetic data |
| Keyword(3) | data augmentation |
| Keyword(4) | adversarial learning |
| Keyword(5) | video classification |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Hana Isoi |
| 1st Author's Affiliation | Ochanomizu University (Ochanomizu Univ.) |
| 2nd Author's Name | Atsuko Takefusa |
| 2nd Author's Affiliation | National Institute of Informatics (NII) |
| 3rd Author's Name | Hidemoto Nakada |
| 3rd Author's Affiliation | National Institute of Advanced Industrial Science and Technology (AIST) |
| 4th Author's Name | Masato Oguchi |
| 4th Author's Affiliation | Ochanomizu University (Ochanomizu Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-05-21 10:30:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2021-5 |
| Volume (vol) | vol.121 |
| Number (no) | no.23 |
| Page | pp.25-30 |
| #Pages | 6 |
| Date of Issue | 2021-05-13 (PRMU) |
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