  Salient object detection has been long studied to identify the most visually
attractive objects in images/videos. Recently, a growing amount of approaches
have been proposed all of which rely on the contour/edge information to improve
detection performance. The edge labels are either put into the loss directly or
used as extra supervision. The edge and body can also be learned separately and
then fused afterward. Both methods either lead to high prediction errors near
the edge or cannot be trained in an end-to-end manner. Another problem is that
existing methods may fail to detect objects of various sizes due to the lack of
efficient and effective feature fusion mechanisms. In this work, we propose to
decompose the saliency detection task into two cascaded sub-tasks, \emph{i.e.},
detail modeling and body filling. Specifically, the detail modeling focuses on
capturing the object edges by supervision of explicitly decomposed detail label
that consists of the pixels that are nested on the edge and near the edge. Then
the body filling learns the body part which will be filled into the detail map
to generate more accurate saliency map. To effectively fuse the features and
handle objects at different scales, we have also proposed two novel multi-scale
detail attention and body attention blocks for precise detail and body
modeling. Experimental results show that our method achieves state-of-the-art
performances on six public datasets.
