Centralized Information Interaction for Salient Object Detection

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.Comment: V2 updates the evaluation results of all methods on the ECSSD dataset (Table. 3 on Page. 8). In V1 we used the old version of ground-truths of ECSSD, which were updated later by its authors. In V2 we use the updated ones instead. Although the numerical evaluation scores of all methods on ECSSD in V1 and V2 vary slightly, the overall trending is still the sam