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- Publisher Website: 10.1109/ICCV.2019.00737
- Scopus: eid_2-s2.0-85081916519
- WOS: WOS:000548549202038
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Conference Paper: Motion Guided Attention for Video Salient Object Detection
Title | Motion Guided Attention for Video Salient Object Detection |
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Authors | |
Issue Date | 2019 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000149 |
Citation | Proceedings of IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, 27 October - 2 November 2019, p. 7273-7282 How to Cite? |
Abstract | Video salient object detection aims at discovering the most visually distinctive objects in a video. How to effectively take object motion into consideration during video salient object detection is a critical issue. Existing state-of-the-art methods either do not explicitly model and harvest motion cues or ignore spatial contexts within optical flow images. In this paper, we develop a multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using two sub-networks, one sub-network for salient object detection in still images and the other for motion saliency detection in optical flow images. We further introduce a series of novel motion guided attention modules, which utilize the motion saliency sub-network to attend and enhance the sub-network for still images. These two sub-networks learn to adapt to each other by end-to-end training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on a wide range of benchmarks. We hope our simple and effective approach will serve as a solid baseline and help ease future research in video salient object detection. Code and models will be made available. |
Persistent Identifier | http://hdl.handle.net/10722/284143 |
ISSN | 2023 SCImago Journal Rankings: 12.263 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Li, H | - |
dc.contributor.author | Chen, G | - |
dc.contributor.author | Li, G | - |
dc.contributor.author | Yu, Y | - |
dc.date.accessioned | 2020-07-20T05:56:26Z | - |
dc.date.available | 2020-07-20T05:56:26Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings of IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, 27 October - 2 November 2019, p. 7273-7282 | - |
dc.identifier.issn | 1550-5499 | - |
dc.identifier.uri | http://hdl.handle.net/10722/284143 | - |
dc.description.abstract | Video salient object detection aims at discovering the most visually distinctive objects in a video. How to effectively take object motion into consideration during video salient object detection is a critical issue. Existing state-of-the-art methods either do not explicitly model and harvest motion cues or ignore spatial contexts within optical flow images. In this paper, we develop a multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using two sub-networks, one sub-network for salient object detection in still images and the other for motion saliency detection in optical flow images. We further introduce a series of novel motion guided attention modules, which utilize the motion saliency sub-network to attend and enhance the sub-network for still images. These two sub-networks learn to adapt to each other by end-to-end training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on a wide range of benchmarks. We hope our simple and effective approach will serve as a solid baseline and help ease future research in video salient object detection. Code and models will be made available. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000149 | - |
dc.relation.ispartof | IEEE International Conference on Computer Vision (ICCV) Proceedings | - |
dc.rights | IEEE International Conference on Computer Vision (ICCV) Proceedings. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.title | Motion Guided Attention for Video Salient Object Detection | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Yu, Y: yzyu@cs.hku.hk | - |
dc.identifier.authority | Yu, Y=rp01415 | - |
dc.identifier.doi | 10.1109/ICCV.2019.00737 | - |
dc.identifier.scopus | eid_2-s2.0-85081916519 | - |
dc.identifier.hkuros | 310941 | - |
dc.identifier.spage | 7273 | - |
dc.identifier.epage | 7282 | - |
dc.identifier.isi | WOS:000548549202038 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1550-5499 | - |