  The U-shape structure has shown its advantage in salient object detection for
efficiently combining multi-scale features. However, most existing U-shape
based methods focused on improving the bottom-up and top-down pathways while
ignoring the connections between them. This paper shows that by centralizing
these connections, we can achieve the cross-scale information interaction among
them, hence obtaining semantically stronger and positionally more precise
features. To inspire the potential of the newly proposed strategy, we further
design a relative global calibration module that can simultaneously process
multi-scale inputs without spatial interpolation. Benefiting from the above
strategy and module, our proposed approach can aggregate features more
effectively while introducing only a few additional parameters. Our approach
can cooperate with various existing U-shape-based salient object detection
methods by substituting the connections between the bottom-up and top-down
pathways. Experimental results demonstrate that our proposed approach performs
favorably against the previous state-of-the-arts on five widely used benchmarks
with less computational complexity. The source code will be publicly available.
