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Bi-attention network for bi-directional salient object detection

Bi-attention network for bi-directional salient object detection Saliency detection models based on neural networks have achieved outstanding results, but there are still problems such as low accuracy of object boundaries and redundant parameters. To alleviate these problems, we make full use of position and contour information from the down-sampling layers, and optimize the detection result layer by layer. First, this paper designs an attention-based adaptive fusion module (AAF), which can suppress the background and highlight the foreground that is more relevant to the detection task. It automatically learns the fusion weights of different features to filter out conflict information. Second, this paper proposes a bi-attention block module which combines reverse attention and positive attention. Third, this paper introduces bi-directional task learning by decomposing the image into high-frequency and low-frequency components. This approach fully exploits the complementary and independent nature of different frequency information. Finally, the proposed method is compared with 14 state-of-the-art methods on 6 datasets, and achieves very competitive results. Additionally, the model size is only 114.19MB, and the inference speed can reach nearly 40 FPS. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Bi-attention network for bi-directional salient object detection

Xu, Cheng; Wang, Hui; Liu, Xianhui; Zhao, Weidong
Applied Intelligence , Volume OnlineFirst – Jun 5, 2023
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References (30)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-023-04648-8
Publisher site
See Article on Publisher Site

Abstract

Saliency detection models based on neural networks have achieved outstanding results, but there are still problems such as low accuracy of object boundaries and redundant parameters. To alleviate these problems, we make full use of position and contour information from the down-sampling layers, and optimize the detection result layer by layer. First, this paper designs an attention-based adaptive fusion module (AAF), which can suppress the background and highlight the foreground that is more relevant to the detection task. It automatically learns the fusion weights of different features to filter out conflict information. Second, this paper proposes a bi-attention block module which combines reverse attention and positive attention. Third, this paper introduces bi-directional task learning by decomposing the image into high-frequency and low-frequency components. This approach fully exploits the complementary and independent nature of different frequency information. Finally, the proposed method is compared with 14 state-of-the-art methods on 6 datasets, and achieves very competitive results. Additionally, the model size is only 114.19MB, and the inference speed can reach nearly 40 FPS.

Journal

Applied Intelligence – Springer Journals

Published: Jun 5, 2023

Keywords: Salient object detection; Bi-attention; Feature fusion; Image boundary

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