Saliency detection network with two-stream encoder and interactive decoder

In recent years, there has been an increasing amount of works on improving the salient object detection performance based on the edge information. However, previous studies applied a parameter-sharing encoder to extract edge features and region features, leading to incomplete regions and blurry edges. In this paper, we focus on the independence and complementarity between salient edges and regions, and propose a novel salient object detection network with two-stream encoder and interactive decoder(TSEID). Specifically, instead of a parameter-sharing encoder, we construct a two-stream encoder consisting of the region extraction branch and the edge extraction branch to balance the feature domain differences between the regions and edges. Meanwhile, we develop a super-pixel segmentation-based method to extract the salient edges, so that the generated distinctive edges will ensure the integrality of edge features. The region features are obtained via a feature pyramid network and hierarchically enhanced with Channel Adaptation (CA) and Spatial Adaptation (SA). Further, the extracted region features and edge features are fed into an interactive decoder to explore their complementarity. Accordingly, a location guidance module (LGM) and an edge compensation module (ECM) are designed to refine object positions, enrich object boundaries, and generate saliency features. Extensive experiments on five challenging benchmarks well demonstrate that our approach outperforms existing deep learning-based methods, including the latest edge-guided ones.