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Multiscale Balanced-Attention Interactive Network for Salient Object Detection

Author

Listed:
  • Haiyan Yang

    (Electronic Information School, Wuhan University, Wuhan 430072, China
    School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China)

  • Rui Chen

    (School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China)

  • Dexiang Deng

    (Electronic Information School, Wuhan University, Wuhan 430072, China)

Abstract

The purpose of saliency detection is to detect significant regions in the image. Great progress on salient object detection has been made using from deep-learning frameworks. How to effectively extract and integrate multiscale information with different depths is an open problem for salient object detection. In this paper, we propose a processing mechanism based on a balanced attention module and interactive residual module. The mechanism addressed the acquisition of the multiscale features by capturing shallow and deep context information. For effective information fusion, a modified bi-directional propagation strategy was adopted. Finally, we used the fused multiscale information to predict saliency features, which were combined to generate the final saliency maps. The experimental results on five benchmark datasets show that the method is on a par with the state of the art for image saliency datasets, especially on the PASCAL-S datasets, where the MAE reaches 0.092, and on the DUT-OMROM datasets, where the F-measure reaches 0.763.

Suggested Citation

  • Haiyan Yang & Rui Chen & Dexiang Deng, 2022. "Multiscale Balanced-Attention Interactive Network for Salient Object Detection," Mathematics, MDPI, vol. 10(3), pages 1-14, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:512-:d:742749
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    Cited by:

    1. Haiyan Yang & Yongxin Chen & Rui Chen & Shuning Liu, 2023. "Hybrid Attention Asynchronous Cascade Network for Salient Object Detection," Mathematics, MDPI, vol. 11(6), pages 1-13, March.

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