  Saliency detection based on the complementary information from RGB images and
depth maps has recently gained great popularity. In this paper, we propose
Complementary Attention and Adaptive Integration Network (CAAI-Net), a novel
RGB-D saliency detection model that integrates complementary attention based
feature concentration and adaptive cross-modal feature fusion into a unified
framework for accurate saliency detection. Specifically, we propose a
context-aware complementary attention (CCA) module, which consists of a feature
interaction component, a complementary attention component, and a
global-context component. The CCA module first utilizes the feature interaction
component to extract rich local context features. The resulting features are
then fed into the complementary attention component, which employs the
complementary attention generated from adjacent levels to guide the attention
at the current layer so that the mutual background disturbances are suppressed
and the network focuses more on the areas with salient objects. Finally, we
utilize a specially-designed adaptive feature integration (AFI) module, which
sufficiently considers the low-quality issue of depth maps, to aggregate the
RGB and depth features in an adaptive manner. Extensive experiments on six
challenging benchmark datasets demonstrate that CAAI-Net is an effective
saliency detection model and outperforms nine state-of-the-art models in terms
of four widely-used metrics. In addition, extensive ablation studies confirm
the effectiveness of the proposed CCA and AFI modules.
