  The goal of this work is to present a systematic solution for RGB-D salient
object detection, which addresses the following three aspects with a unified
framework: modal-specific representation learning, complementary cue selection
and cross-modal complement fusion. To learn discriminative modal-specific
features, we propose a hierarchical cross-modal distillation scheme, in which
the well-learned source modality provides supervisory signals to facilitate the
learning process for the new modality. To better extract the complementary
cues, we formulate a residual function to incorporate complements from the
paired modality adaptively. Furthermore, a top-down fusion structure is
constructed for sufficient cross-modal interactions and cross-level
transmissions. The experimental results demonstrate the effectiveness of the
proposed cross-modal distillation scheme in zero-shot saliency detection and
pre-training on a new modality, as well as the advantages in selecting and
fusing cross-modal/cross-level complements.
