  In this paper, we study the problem of salient object detection (SOD) for
RGB-D images using both color and depth information.A major technical challenge
in performing salient object detection fromRGB-D images is how to fully
leverage the two complementary data sources. Current works either simply
distill prior knowledge from the corresponding depth map for handling the
RGB-image or blindly fuse color and geometric information to generate the
coarse depth-aware representations, hindering the performance of RGB-D saliency
detectors.In this work, we introduceCascade Graph Neural Networks(Cas-Gnn),a
unified framework which is capable of comprehensively distilling and reasoning
the mutual benefits between these two data sources through a set of cascade
graphs, to learn powerful representations for RGB-D salient object detection.
Cas-Gnn processes the two data sources individually and employs a novelCascade
Graph Reasoning(CGR) module to learn powerful dense feature embeddings, from
which the saliency map can be easily inferred. Contrast to the previous
approaches, the explicitly modeling and reasoning of high-level relations
between complementary data sources allows us to better overcome challenges such
as occlusions and ambiguities. Extensive experiments demonstrate that Cas-Gnn
achieves significantly better performance than all existing RGB-DSOD approaches
on several widely-used benchmarks.
