  RGB-D salient object detection (SOD) is usually formulated as a problem of
classification or regression over two modalities, i.e., RGB and depth. Hence,
effective RGBD feature modeling and multi-modal feature fusion both play a
vital role in RGB-D SOD. In this paper, we propose a depth-sensitive RGB
feature modeling scheme using the depth-wise geometric prior of salient
objects. In principle, the feature modeling scheme is carried out in a
depth-sensitive attention module, which leads to the RGB feature enhancement as
well as the background distraction reduction by capturing the depth geometry
prior. Moreover, to perform effective multi-modal feature fusion, we further
present an automatic architecture search approach for RGB-D SOD, which does
well in finding out a feasible architecture from our specially designed
multi-modal multi-scale search space. Extensive experiments on seven standard
benchmarks demonstrate the effectiveness of the proposed approach against the
state-of-the-art.
