  The extensive research leveraging RGB-D information has been exploited in
salient object detection. However, salient visual cues appear in various scales
and resolutions of RGB images due to semantic gaps at different feature levels.
Meanwhile, similar salient patterns are available in cross-modal depth images
as well as multi-scale versions. Cross-modal fusion and multi-scale refinement
are still an open problem in RGB-D salient object detection task. In this
paper, we begin by introducing top-down and bottom-up iterative refinement
architecture to leverage multi-scale features, and then devise attention based
fusion module (ABF) to address on cross-modal correlation. We conduct extensive
experiments on seven public datasets. The experimental results show the
effectiveness of our devised method
