Depth maps contain geometric clues for assisting Salient Object Detection
(SOD). In this paper, we propose a novel Cross-Modal Weighting (CMW) strategy
to encourage comprehensive interactions between RGB and depth channels for
RGB-D SOD. Specifically, three RGB-depth interaction modules, named CMW-L,
CMW-M and CMW-H, are developed to deal with respectively low-, middle- and
high-level cross-modal information fusion. These modules use Depth-to-RGB
Weighing (DW) and RGB-to-RGB Weighting (RW) to allow rich cross-modal and
cross-scale interactions among feature layers generated by different network
blocks. To effectively train the proposed Cross-Modal Weighting Network
(CMWNet), we design a composite loss function that summarizes the errors
between intermediate predictions and ground truth over different scales. With
all these novel components working together, CMWNet effectively fuses
information from RGB and depth channels, and meanwhile explores object
localization and details across scales. Thorough evaluations demonstrate CMWNet
consistently outperforms 15 state-of-the-art RGB-D SOD methods on seven popular
benchmarks.

本研究提出一种跨模型加权策略（CMW），采用三个 RGB - 深度交互模块（CMW-L、CMW-M 和 CMW-H）来分别处理低、中、高级别跨模式融合信息，并使用深度到 RGB 加权（DW）和 RGB 到 RGB 加权（RW）来允许由不同网络块生成的特征层之间进行丰富的跨模式和跨尺度交互，有效地融合 RGB 和深度通道的信息，同时探索不同尺度的物体定位和细节，实验结果表明，该方法在七个常用基准测试中优于 15 种现有的 RGB-D SOD 方法。