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RGB-D salient object detection (SOD) is usually formulated as a problem ofclassification or regression over two modalities, i.e., RGB and depth. Hence,effective RGBD feature modeling and multi-modal feature fusion both play avital role in RGB-D SOD. In this paper, we propose a depth-sensitive RGBfeature modeling scheme using the depth-wise geometric prior of salientobjects. In principle, the feature modeling scheme is carried out in adepth-sensitive attention module, which leads to the RGB feature enhancement aswell as the background distraction reduction by capturing the depth geometryprior. Moreover, to perform effective multi-modal feature fusion, we furtherpresent an automatic architecture search approach for RGB-D SOD, which doeswell in finding out a feasible architecture from our specially designedmulti-modal multi-scale search space. Extensive experiments on seven standardbenchmarks demonstrate the effectiveness of the proposed approach against thestate-of-the-art.

Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion