  RGB-D salient object detection (SOD) recently has attracted increasing
research interest by benefiting conventional RGB SOD with extra depth
information. However, existing RGB-D SOD models often fail to perform well in
terms of both efficiency and accuracy, which hinders their potential
applications on mobile devices and real-world problems. An underlying challenge
is that the model accuracy usually degrades when the model is simplified to
have few parameters. To tackle this dilemma and also inspired by the fact that
depth quality is a key factor influencing the accuracy, we propose a novel
depth quality-inspired feature manipulation (DQFM) process, which is efficient
itself and can serve as a gating mechanism for filtering depth features to
greatly boost the accuracy. DQFM resorts to the alignment of low-level RGB and
depth features, as well as holistic attention of the depth stream to explicitly
control and enhance cross-modal fusion. We embed DQFM to obtain an efficient
light-weight model called DFM-Net, where we also design a tailored depth
backbone and a two-stage decoder for further efficiency consideration.
Extensive experimental results demonstrate that our DFM-Net achieves
state-of-the-art accuracy when comparing to existing non-efficient models, and
meanwhile runs at 140ms on CPU (2.2$\times$ faster than the prior fastest
efficient model) with only $\sim$8.5Mb model size (14.9% of the prior
lightest). Our code will be available at https://github.com/zwbx/DFM-Net.
