  Salient object detection (SOD) has been well studied in recent years,
especially using deep neural networks. However, SOD with RGB and RGB-D images
is usually treated as two different tasks with different network structures
that need to be designed specifically. In this paper, we proposed a unified and
efficient structure with a cross-attention context extraction (CRACE) module to
address both tasks of SOD efficiently. The proposed CRACE module receives and
appropriately fuses two (for RGB SOD) or three (for RGB-D SOD) inputs. The
simple unified feature pyramid network (FPN)-like structure with CRACE modules
conveys and refines the results under the multi-level supervisions of saliency
and boundaries. The proposed structure is simple yet effective; the rich
context information of RGB and depth can be appropriately extracted and fused
by the proposed structure efficiently. Experimental results show that our
method outperforms other state-of-the-art methods in both RGB and RGB-D SOD
tasks on various datasets and in terms of most metrics.
