  Albeit intensively studied, false prediction and unclear boundaries are still
major issues of salient object detection. In this paper, we propose a Region
Refinement Network (RRN), which recurrently filters redundant information and
explicitly models boundary information for saliency detection. Different from
existing refinement methods, we propose a Region Refinement Module (RRM) that
optimizes salient region prediction by incorporating supervised attention masks
in the intermediate refinement stages. The module only brings a minor increase
in model size and yet significantly reduces false predictions from the
background. To further refine boundary areas, we propose a Boundary Refinement
Loss (BRL) that adds extra supervision for better distinguishing foreground
from background. BRL is parameter free and easy to train. We further observe
that BRL helps retain the integrity in prediction by refining the boundary.
Extensive experiments on saliency detection datasets show that our refinement
module and loss bring significant improvement to the baseline and can be easily
applied to different frameworks. We also demonstrate that our proposed model
generalizes well to portrait segmentation and shadow detection tasks.
