By integrating complementary information from RGB image and depth map, the
ability of salient object detection (SOD) for complex and challenging scenes
can be improved. In recent years, the important role of Convolutional Neural
Networks (CNNs) in feature extraction and cross-modality interaction has been
fully explored, but it is still insufficient in modeling global long-range
dependencies of self-modality and cross-modality. To this end, we introduce
CNNs-assisted Transformer architecture and propose a novel RGB-D SOD network
with Point-aware Interaction and CNN-induced Refinement (PICR-Net). On the one
hand, considering the prior correlation between RGB modality and depth
modality, an attention-triggered cross-modality point-aware interaction (CmPI)
module is designed to explore the feature interaction of different modalities
with positional constraints. On the other hand, in order to alleviate the block
effect and detail destruction problems brought by the Transformer naturally, we
design a CNN-induced refinement (CNNR) unit for content refinement and
supplementation. Extensive experiments on five RGB-D SOD datasets show that the
proposed network achieves competitive results in both quantitative and
qualitative comparisons.

通过将 RGB 图像和深度图的互补信息整合，提升了对复杂和具有挑战性场景的显著对象检测（SOD）的能力。我们引入了 CNN 辅助 Transformer 架构，并提出了一种新的 RGB-D SOD 网络，具备点感知交互和 CNN 诱导细化的特点。在实验证明，所提出的网络在定量和定性比较中取得了竞争性的结果。