  There are two main issues in RGB-D salient object detection: (1) how to
effectively integrate the complementarity from the cross-modal RGB-D data; (2)
how to prevent the contamination effect from the unreliable depth map. In fact,
these two problems are linked and intertwined, but the previous methods tend to
focus only on the first problem and ignore the consideration of depth map
quality, which may yield the model fall into the sub-optimal state. In this
paper, we address these two issues in a holistic model synergistically, and
propose a novel network named DPANet to explicitly model the potentiality of
the depth map and effectively integrate the cross-modal complementarity. By
introducing the depth potentiality perception, the network can perceive the
potentiality of depth information in a learning-based manner, and guide the
fusion process of two modal data to prevent the contamination occurred. The
gated multi-modality attention module in the fusion process exploits the
attention mechanism with a gate controller to capture long-range dependencies
from a cross-modal perspective. Experimental results compared with 15
state-of-the-art methods on 8 datasets demonstrate the validity of the proposed
approach both quantitatively and qualitatively.
