Multiscale convolutional neural network (CNN) has demonstrated remarkable
capabilities in solving various vision problems. However, fusing features of
different scales alwaysresults in large model sizes, impeding the application
of multiscale CNNs in RGB-D saliency detection. In this paper, we propose a
customized feature fusion module, called Saliency Enhanced Feature Fusion
(SEFF), for RGB-D saliency detection. SEFF utilizes saliency maps of the
neighboring scales to enhance the necessary features for fusing, resulting in
more representative fused features. Our multiscale RGB-D saliency detector uses
SEFF and processes images with three different scales. SEFF is used to fuse the
features of RGB and depth images, as well as the features of decoders at
different scales. Extensive experiments on five benchmark datasets have
demonstrated the superiority of our method over ten SOTA saliency detectors.

本文针对 RGB-D 显著性检测问题，提出了一种定制化的特征融合模块 SEFF，通过利用邻近尺度的显著性图来增强融合所需的特征，得到更具代表性的融合特征。我们的多尺度 RGB-D 显著性检测器使用 SEFF 处理三个不同尺度的图像，并将 RGB 和深度图像的特征以及不同尺度解码器的特征进行融合。在五个基准数据集上进行的大量实验证明了我们方法在显著性检测上的优越性。