RGB-D salient object detection (SOD) aims to detect the prominent regions by
jointly modeling RGB and depth information. Most RGB-D SOD methods apply the
same type of backbones and fusion modules to identically learn the
multimodality and multistage features. However, these features contribute
differently to the final saliency results, which raises two issues: 1) how to
model discrepant characteristics of RGB images and depth maps; 2) how to fuse
these cross-modality features in different stages. In this paper, we propose a
high-order discrepant interaction network (HODINet) for RGB-D SOD. Concretely,
we first employ transformer-based and CNN-based architectures as backbones to
encode RGB and depth features, respectively. Then, the high-order
representations are delicately extracted and embedded into spatial and channel
attentions for cross-modality feature fusion in different stages. Specifically,
we design a high-order spatial fusion (HOSF) module and a high-order channel
fusion (HOCF) module to fuse features of the first two and the last two stages,
respectively. Besides, a cascaded pyramid reconstruction network is adopted to
progressively decode the fused features in a top-down pathway. Extensive
experiments are conducted on seven widely used datasets to demonstrate the
effectiveness of the proposed approach. We achieve competitive performance
against 24 state-of-the-art methods under four evaluation metrics.

本文提出了一种高阶差异交互网络（HODINet）用于 RGB-D 显著目标检测，通过使用 Transformer 和 CNN 作为主干来编码 RGB 和深度特征，并将高阶表示嵌入到空间和通道关注力中以在不同阶段融合跨模式特征，实验证明该方法在四种评估指标下与 24 种现有方法相比具有竞争性的性能。