  Recent RGBD-based models for saliency detection have attracted research
attention. The depth clues such as boundary clues, surface normal, shape
attribute, etc., contribute to the identification of salient objects with
complicated scenarios. However, most RGBD networks require multi-modalities
from the input side and feed them separately through a two-stream design, which
inevitably results in extra costs on depth sensors and computation. To tackle
these inconveniences, we present in this paper a novel fusion design named
modality-guided subnetwork (MGSnet). It has the following superior designs: 1)
Our model works for both RGB and RGBD data, and dynamically estimating depth if
not available. Taking the inner workings of depth-prediction networks into
account, we propose to estimate the pseudo-geometry maps from RGB input -
essentially mimicking the multi-modality input. 2) Our MGSnet for RGB SOD
results in real-time inference but achieves state-of-the-art performance
compared to other RGB models. 3) The flexible and lightweight design of MGS
facilitates the integration into RGBD two-streaming models. The introduced
fusion design enables a cross-modality interaction to enable further progress
but with a minimal cost.
