HODINet: High-Order Discrepant Interaction Network for RGB-D Salient
  Object Detection
Kang Yi Jing Xu Xiao Jin Fu Guo Yan-Feng Wu
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.