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 we use the progressive predictions from the well-learned source modality to supervise learning feature hierarchies and inference in the new modality. To better select 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 cross-level interactions. The experimental results demonstrate the effectiveness of the proposed cross-modal distillation scheme in learning from a new modality, the advantages of the proposed multi-modal fusion pattern in selecting and fusing cross-modal complements, and the generalization of the proposed designs in different tasks.

这项工作的目的是为RGB-D显着物体检测提供一种系统的解决方案，该解决方案使用统一的框架解决以下三个方面：模态特定表示学习，互补提示选择和跨模态互补融合。为了学习区分性的模态特定特征，我们提出了一种分层的交叉模态提纯方案，其中我们使用来自经验丰富的源模态的渐进预测来监督学习特征的层次结构和对新模态的推断。为了更好地选择互补线索，我们制定了残差函数以自适应地结合来自配对模态的补语。此外，构造了自上而下的融合结构以实现足够的跨模式跨层交互。