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Deep salient object detection with contextual information guidance

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Liu, Yi, Han, Jungong, Zhang, Qiang and Shan, Caifeng (2019) Deep salient object detection with contextual information guidance. IEEE Transactions on Image Processing, 29 . pp. 360-374. doi:10.1109/TIP.2019.2930906

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Official URL: http://dx.doi.org/10.1109/TIP.2019.2930906

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Abstract

Integration of multi-level contextual information, such as feature maps and side outputs, is crucial for Convolutional Neural Networks (CNNs) based salient object detection. However, most existing methods either simply concatenate multi-level feature maps or calculate element-wise addition of multi-level side outputs, thus failing to take full advantages of them. In this work, we propose a new strategy for guiding multi-level contextual information integration, where feature maps and side outputs across layers are fully engaged. Specifically, shallower-level feature maps are guided by the deeper-level side outputs to learn more accurate properties of the salient object. In turn, the deeper-level side outputs can be propagated to high-resolution versions with spatial details complemented by means of shallower-level feature maps. Moreover, a group convolution module is proposed with the aim to achieve high-discriminative feature maps, in which the backbone feature maps are divided into a number of groups and then the convolution is applied to the channels of backbone feature maps within each group. Eventually, the group convolution module is incorporated in the guidance module to further promote the guidance role. Experiments on three public benchmark datasets verify the effectiveness and superiority of the proposed method over the state-of-the-art methods.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH): Neural networks (Computer science) , Computer vision , Pattern perception
Journal or Publication Title: IEEE Transactions on Image Processing
Publisher: IEEE
ISSN: 1057-7149
Official Date: 30 July 2019
Dates:
DateEvent
30 July 2019Published
16 July 2019Accepted
Volume: 29
Page Range: pp. 360-374
DOI: 10.1109/TIP.2019.2930906
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author 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.
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
6177330[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
JBZ170401Central Universities in ChinaUNSPECIFIED
201806960044China Scholarship Councilhttp://dx.doi.org/10.13039/501100004543
Postgraduate Innovation FundXidian Universityhttp://dx.doi.org/10.13039/501100005320

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