  The main purpose of RGB-D salient object detection (SOD) is how to better
integrate and utilize cross-modal fusion information. In this paper, we explore
these issues from a new perspective. We integrate the features of different
modalities through densely connected structures and use their mixed features to
generate dynamic filters with receptive fields of different sizes. In the end,
we implement a kind of more flexible and efficient multi-scale cross-modal
feature processing, i.e. dynamic dilated pyramid module. In order to make the
predictions have sharper edges and consistent saliency regions, we design a
hybrid enhanced loss function to further optimize the results. This loss
function is also validated to be effective in the single-modal RGB SOD task. In
terms of six metrics, the proposed method outperforms the existing twelve
methods on eight challenging benchmark datasets. A large number of experiments
verify the effectiveness of the proposed module and loss function. Our code,
model and results are available at \url{https://github.com/lartpang/HDFNet}.
