  Most existing RGB-D salient object detection (SOD) models require large
computational costs and memory consumption to accurately detect the salient
objects. This limits the real-life applications of these RGB-D SOD models. To
address this issue, a novel lightweight RGB-D SOD model is presented in this
paper. Different from most existing models which usually employ the two-stream
or single-stream structure, we propose to employ the middle-level fusion
structure for designing lightweight RGB-D SOD model, due to the fact that the
middle-level fusion structure can simultaneously exploit the modality-shared
and modality-specific information as the two-stream structure and can
significantly reduce the network's parameters as the single-stream structure.
Based on this structure, a novel information-aware multi-modal feature fusion
(IMFF) module is first designed to effectively capture the cross-modal
complementary information. Then, a novel lightweight feature-level and
decision-level feature fusion (LFDF) module is designed to aggregate the
feature-level and the decision-level saliency information in different stages
with less parameters. With IMFF and LFDF modules incorporated in the
middle-level fusion structure, our proposed model has only 3.9M parameters and
runs at 33 FPS. Furthermore, the experimental results on several benchmark
datasets verify the effectiveness and superiority of the proposed method over
some state-of-the-art methods.
