  The semantic representation of deep features is essential for image context
understanding, and effective fusion of features with different semantic
representations can significantly improve the model's performance on salient
object detection. In this paper, a novel method called MPI is proposed for
salient object detection. Firstly, a multi-receptive enhancement module (MRE)
is designed to effectively expand the receptive fields of features from
different layers and generate features with different receptive fields. MRE can
enhance the semantic representation and improve the model's perception of the
image context, which enables the model to locate the salient object accurately.
Secondly, in order to reduce the reuse of redundant information in the complex
top-down fusion method and weaken the differences between semantic features, a
relatively simple but effective parallel fusion strategy (PFS) is proposed. It
allows multi-scale features to better interact with each other, thus improving
the overall performance of the model. Experimental results on multiple datasets
demonstrate that the proposed method outperforms state-of-the-art methods under
different evaluation metrics.
