Complementary characteristics fusion network for weakly supervised salient object detection

作者：

Highlights：

• We propose an edge fusion module using the local and high-level semantic information.

• Feature correlation module is employed to make full use of the complementary different features.

• We propose a self-supervised salient detection loss to learn structural information.

• The proposed method performs competitive against the state-of-the-art methods.

摘要

•We propose an edge fusion module using the local and high-level semantic information.•Feature correlation module is employed to make full use of the complementary different features.•We propose a self-supervised salient detection loss to learn structural information.•The proposed method performs competitive against the state-of-the-art methods.

论文关键词：Salient object detection,Weakly supervised learning,Edge fusion module,Feature correlation module,Self-supervised salient detection loss

论文评审过程：Received 23 January 2022, Revised 4 July 2022, Accepted 16 August 2022, Available online 23 August 2022, Version of Record 31 August 2022.

论文官网地址：https://doi.org/10.1016/j.imavis.2022.104536