  Salient object detection (SOD) on RGB and depth images has attracted more and
more research interests, due to its effectiveness and the fact that depth cues
can now be conveniently captured. Existing RGB-D SOD models usually adopt
different fusion strategies to learn a shared representation from the two
modalities (\ie, RGB and depth), while few methods explicitly consider how to
preserve modality-specific characteristics. In this study, we propose a novel
framework, termed SPNet} (Specificity-preserving network), which benefits SOD
performance by exploring both the shared information and modality-specific
properties (\eg, specificity). Specifically, we propose to adopt two
modality-specific networks and a shared learning network to generate individual
and shared saliency prediction maps, respectively. To effectively fuse
cross-modal features in the shared learning network, we propose a
cross-enhanced integration module (CIM) and then propagate the fused feature to
the next layer for integrating cross-level information. Moreover, to capture
rich complementary multi-modal information for boosting the SOD performance, we
propose a multi-modal feature aggregation (MFA) module to integrate the
modality-specific features from each individual decoder into the shared
decoder. By using a skip connection, the hierarchical features between the
encoder and decoder layers can be fully combined. Extensive experiments
demonstrate that our~\ours~outperforms cutting-edge approaches on six popular
RGB-D SOD and three camouflaged object detection benchmarks. The project is
publicly available at: https://github.com/taozh2017/SPNet.
