  Depth cues with affluent spatial information have been proven beneficial in
boosting salient object detection (SOD), while the depth quality directly
affects the subsequent SOD performance. However, it is inevitable to obtain
some low-quality depth cues due to limitations of its acquisition devices,
which can inhibit the SOD performance. Besides, existing methods tend to
combine RGB images and depth cues in a direct fusion or a simple fusion module,
which makes they can not effectively exploit the complex correlations between
the two sources. Moreover, few methods design an appropriate module to fully
fuse multi-level features, resulting in cross-level feature interaction
insufficient. To address these issues, we propose a novel Multi-level
Cross-modal Interaction Network (MCINet) for RGB-D based SOD. Our MCI-Net
includes two key components: 1) a cross-modal feature learning network, which
is used to learn the high-level features for the RGB images and depth cues,
effectively enabling the correlations between the two sources to be exploited;
and 2) a multi-level interactive integration network, which integrates
multi-level cross-modal features to boost the SOD performance. Extensive
experiments on six benchmark datasets demonstrate the superiority of our
MCI-Net over 14 state-of-the-art methods, and validate the effectiveness of
different components in our MCI-Net. More important, our MCI-Net significantly
improves the SOD performance as well as has a higher FPS.
