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。

HODINet: RGB-D 显著物体检测高阶不一致交互网络

HODINet: High-Order Discrepant Interaction Network for RGB-D Salient  Object Detection

Recent non-local self-attention methods have proven to be effective in
capturing long-range dependencies for semantic segmentation. These methods
usually form a similarity map of RC*C (by compressing spatial dimensions) or
RHW*HW (by compressing channels) to describe the feature relations along either
channel or spatial dimensions, where C is the number of channels, H and W are
the spatial dimensions of the input feature map. However, such practices tend
to condense feature dependencies along the other dimensions,hence causing
attention missing, which might lead to inferior results for small/thin
categories or inconsistent segmentation inside large objects. To address this
problem, we propose anew approach, namely Fully Attentional Network (FLANet),to
encode both spatial and channel attentions in a single similarity map while
maintaining high computational efficiency. Specifically, for each channel map,
our FLANet can harvest feature responses from all other channel maps, and the
associated spatial positions as well, through a novel fully attentional module.
Our new method has achieved state-of-the-art performance on three challenging
semantic segmentation datasets,i.e., 83.6%, 46.99%, and 88.5% on the Cityscapes
test set,the ADE20K validation set, and the PASCAL VOC test set,respectively.

该研究提出了 FLANet 方法，可以通过单个相似度图来同时编码空间和通道注意力，并在三个具有挑战性的语义分割数据集上最先进的性能达到 83.6％，46.99％和 88.5％。