Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 29, Issue -, Pages 360-374Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2019.2930906
Keywords
Feature extraction; Object detection; Convolution; Semantics; Saliency detection; Neural networks; Object recognition; Salient object detection; convolutional neural networks (CNNs); group convolution; multi-level contextual information integration
Funding
- National Natural Science Foundation of China [61773301]
- Science Foundation of Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory [181101]
- Funds of China Scholarship Council [201806960044]
- Postgraduate Innovation Fund of Xidian University
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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 paper, 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.
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