  Effective fusion of different types of features is the key to salient object
detection. The majority of existing network structure design is based on the
subjective experience of scholars and the process of feature fusion does not
consider the relationship between the fused features and highest-level
features. In this paper, we focus on the feature relationship and propose a
novel global attention unit, which we term the "perception- and-regulation"
(PR) block, that adaptively regulates the feature fusion process by explicitly
modeling interdependencies between features. The perception part uses the
structure of fully-connected layers in classification networks to learn the
size and shape of objects. The regulation part selectively strengthens and
weakens the features to be fused. An imitating eye observation module (IEO) is
further employed for improving the global perception ability of the network.
The imitation of foveal vision and peripheral vision enables IEO to scrutinize
highly detailed objects and to organize the broad spatial scene to better
segment objects. Sufficient experiments conducted on SOD datasets demonstrate
that the proposed method performs favorably against 22 state-of-the-art
methods.
