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There are two main issues in RGB-D salient object detection: (1) how toeffectively 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 tofocus only on the first problem and ignore the consideration of depth mapquality, which may yield the model fall into the sub-optimal state. In thispaper, we address these two issues in a holistic model synergistically, andpropose a novel network named DPANet to explicitly model the potentiality ofthe depth map and effectively integrate the cross-modal complementarity. Byintroducing the depth potentiality perception, the network can perceive thepotentiality of depth information in a learning-based manner, and guide thefusion process of two modal data to prevent the contamination occurred. Thegated multi-modality attention module in the fusion process exploits theattention mechanism with a gate controller to capture long-range dependenciesfrom a cross-modal perspective. Experimental results compared with 15state-of-the-art methods on 8 datasets demonstrate the validity of the proposedapproach both quantitatively and qualitatively.

DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection