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| Paper Abstract and Keywords | ||
| Presentation | 2022-03-09 09:45 Improving Weakly Supervised Instance Segmentation by Encoding Motion Information via Optical Flow Jun Ikeda, Junichiro Mori (UT)  IMQ2021-15 IE2021-77 MVE2021-44 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Weakly supervised instance segmentation is an important task that can significantly reduce the annotation cost of model training. Previous works have been using appearance information obtained from a single image for detection and segmentation. However, it poses the challenge of identifying objects with non-discriminative appearance. In this paper, we tackle this problem by using motion information from image sequences. Specifically, we propose a two-stream encoder that appropriately leverages appearance and motion features extracted from images and optical flows. In addition, we employ a pairwise loss that considers both appearance and motion information to supervise segmentation. We conduct extensive evaluations on the YouTube-VIS 2019 dataset. Our results demonstrate that the proposed method improves the AP of the state-of-the-art method by 3.1. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | instance segmentation / weakly supervised learning / optical flow / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 423, MVE2021-44, pp. 27-32, March 2022. | |
| Paper # | MVE2021-44 | |
| Date of Issue | 2022-03-02 (IMQ, IE, MVE) | |
| 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 | IMQ2021-15 IE2021-77 MVE2021-44 |
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| Conference Information | |
| Committee | CQ IMQ MVE IE |
| Conference Date | 2022-03-09 - 2022-03-11 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online (Zoom) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Media of five senses, Multimedia, Media experience, Picture codinge, Image media quality, Network,quality and reliability, etc |
| Paper Information | |
| Registration To | MVE |
| Conference Code | 2022-03-CQ-IMQ-MVE-IE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Improving Weakly Supervised Instance Segmentation by Encoding Motion Information via Optical Flow |
| Sub Title (in English) | |
| Keyword(1) | instance segmentation |
| Keyword(2) | weakly supervised learning |
| Keyword(3) | optical flow |
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| 1st Author's Name | Jun Ikeda |
| 1st Author's Affiliation | The University of Tokyo (UT) |
| 2nd Author's Name | Junichiro Mori |
| 2nd Author's Affiliation | The University of Tokyo (UT) |
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| Speaker | Author-1 |
| Date Time | 2022-03-09 09:45:00 |
| Presentation Time | 25 minutes |
| Registration for | MVE |
| Paper # | IMQ2021-15, IE2021-77, MVE2021-44 |
| Volume (vol) | vol.121 |
| Number (no) | no.420(IMQ), no.422(IE), no.423(MVE) |
| Page | pp.27-32 |
| #Pages | 6 |
| Date of Issue | 2022-03-02 (IMQ, IE, MVE) |
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