Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2019

Abstract

This paper presents a new method for detecting salient objects in images using convolutional neural networks (CNNs). The proposed network, named PAGE-Net, offers two key contributions. The first is the exploitation of an essential pyramid attention structure for salient object detection. This enables the network to concentrate more on salient regions while considering multi-scale saliency information. Such a stacked attention design provides a powerful tool to efficiently improve the representation ability of the corresponding network layer with an enlarged receptive field. The second contribution lies in the emphasis on the importance of salient edges. Salient edge information offers a strong cue to better segment salient objects and refine object boundaries. To this end, our model is equipped with a salient edge detection module, which is learned for precise salient boundary estimation. This encourages better edge-preserving salient object segmentation. Exhaustive experiments confirm that the proposed pyramid attention and salient edges are effective for salient object detection. We show that our deep saliency model outperforms state-of-the-art approaches for several benchmarks with a fast processing speed (25fps on one GPU).

Keywords

Image and Video Synthesis, Low-level Vision

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): June 15-20, Long Beach, CA: Proceedings

First Page

1448

Last Page

1457

ISBN

9781728132938

Identifier

10.1109/CVPR.2019.00154

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/CVPR.2019.00154

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