  To get more accurate saliency maps, recent methods mainly focus on
aggregating multi-level features from fully convolutional network (FCN) and
introducing edge information as auxiliary supervision. Though remarkable
progress has been achieved, we observe that the closer the pixel is to the
edge, the more difficult it is to be predicted, because edge pixels have a very
imbalance distribution. To address this problem, we propose a label decoupling
framework (LDF) which consists of a label decoupling (LD) procedure and a
feature interaction network (FIN). LD explicitly decomposes the original
saliency map into body map and detail map, where body map concentrates on
center areas of objects and detail map focuses on regions around edges. Detail
map works better because it involves much more pixels than traditional edge
supervision. Different from saliency map, body map discards edge pixels and
only pays attention to center areas. This successfully avoids the distraction
from edge pixels during training. Therefore, we employ two branches in FIN to
deal with body map and detail map respectively. Feature interaction (FI) is
designed to fuse the two complementary branches to predict the saliency map,
which is then used to refine the two branches again. This iterative refinement
is helpful for learning better representations and more precise saliency maps.
Comprehensive experiments on six benchmark datasets demonstrate that LDF
outperforms state-of-the-art approaches on different evaluation metrics.
