  Recent works on salient object detection have made use of multi-scale
features in a way such that high-level features and low-level features can
collaborate in locating salient objects. Many of the previous methods have
achieved great performance in salient object detection. By merging the
high-level and low-level features, a large number of feature information can be
extracted. Generally, they are doing these in a one-way framework, and
interweaving the variable features all the way to the final feature output.
Which may cause some blurring or inaccurate localization of saliency maps. To
overcome these difficulties, we introduce a multistep feature aggregation
(MSFA) framework for salient object detection, which is composed of three
modules, including the Diverse Reception (DR) module, multiscale interaction
(MSI) module and Feature Enhancement (FE) module to accomplish better
multi-level feature fusion. Experimental results on six benchmark datasets
demonstrate that MSFA achieves state-of-the-art performance.
