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.

该论文旨在提出一个系统性的解决方案来解决 RGB-D 突出物检测问题，该方案通过模态特定表示学习、互补线索选择和跨模态补充融合等三个方面进行统一的处理，并构建了一种自适应的残差函数来融合跨模态信息以实现足够的跨模态交互和跨层传输支持。