  Salient Object Detection (SOD) domain using RGB-D data has lately emerged
with some current models' adequately precise results. However, they have
restrained generalization abilities and intensive computational complexity. In
this paper, inspired by the best background/foreground separation abilities of
deformable convolutions, we employ them in our Densely Deformable Network
(DDNet) to achieve efficient SOD. The salient regions from densely deformable
convolutions are further refined using transposed convolutions to optimally
generate the saliency maps. Quantitative and qualitative evaluations using the
recent SOD dataset against 22 competing techniques show our method's efficiency
and effectiveness. We also offer evaluation using our own created
cross-dataset, surveillance-SOD (S-SOD), to check the trained models' validity
in terms of their applicability in diverse scenarios. The results indicate that
the current models have limited generalization potentials, demanding further
research in this direction. Our code and new dataset will be publicly available
at https://github.com/tanveer-hussain/EfficientSOD
