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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 . doi:10.1109/TIP.2019.2930906 (In Press)
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WRAP-deep-salient-object-detection-contextual-guidance-Han-2019.pdf - Accepted Version - Requires a PDF viewer. Download (3437Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TIP.2019.2930906
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 | |||||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science > 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: |
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| Date of first compliant deposit: | 2 August 2019 | |||||||||||||||
| DOI: | 10.1109/TIP.2019.2930906 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | In Press | |||||||||||||||
| Publisher Statement: | © 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: |
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