Open Access

REVIEW

PGCA-Net: Progressively Aggregating Hierarchical Features with the Pyramid Guided Channel Attention for Saliency Detection

Jiajie Mai¹, Xuemiao Xu^2,*, Guorong Xiao³, Zijun Deng², Jiaxing Chen²

1 Department of Telecommunications Engineering and Management, Beijing University of Posts and Telecommunication, Beijing100876, China
2 School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
3 Key Laboratory of Science & Technology and Finance, Guangdong University of Finance, Guangzhou 510521, China

* Corresponding Author: Xuemiao Xu,

Intelligent Automation & Soft Computing 2020, 26(4), 847-855. https://doi.org/10.32604/iasc.2020.010119

Abstract

The Salient object detection aims to segment out the most visually distinctive objects in an image, which is a challenging task in computer vision. In this paper, we present the PGCA-Net equipped with the pyramid guided channel attention fusion block (PGCAFB) for the saliency detection task. Given an input image, the hierarchical features are extracted using a deep convolutional neural network (DCNN), then starting from the highest-level semantic features, we stage-by-stage restore the spatial saliency details by aggregating the lowerlevel detailed features. Since for the weak discriminative ability of the shallow detailed features, directly introducing them to the semantic features will only lead to sub-optimal results. Thus, we take a novel pyramid channel attention mechanism to attend to the useful detailed shallow feature channels before aggregation. The experimental results show that our proposed method outperforms its competitors on 5 benchmark testing sets.

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Keywords

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