# Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
## 2 Related Work
### RGB-D Salient Object Detection  
In order to overcome the limitations of RGB saliency object detection, RGB-D saliency object detection emerged. RGB-D saliency object detection not only utilizes the color and texture information of RGB images but also combines the geometric and structural information provided by depth images, enabling effective detection of salient objects in more complex environments. Depth information can provide additional clues about the three-dimensional structure of the scene, helping to more accurately locate salient objects in the presence of complex backgrounds and occlusions. Early RGB-D saliency object detection methods mainly focused on how to effectively fuse RGB and depth information. The approach of feeding RGB images and depth images into two separate networks for feature extraction and fusion and then passing the high-level features to a decoder to obtain the final saliency map [27; 28] does not fully exploit the unique properties of depth information. In response, [PERSON] et al. [29] proposed the CPFP model, which introduces contrast enhancement for depth information within the CNN architecture and then integrates the enhanced depth with RGB features to generate the saliency map. However, this approach does not fully utilize the advantages of the dual-stream network and may result in blurred edges in the generated saliency map. To address this, [PERSON] et al. [30] proposed an adaptive fusion scheme that implements efficient detection through the design of a dual-stream convolutional neural network and an adaptive fusion module. [PERSON] et al. [31] designed the SSF model, which allows the RGB and depth information to complement each other in detail, refining the features and sharpening the boundaries by adding an edge loss function. However, these methods generally perform a simple fusion of the two modalities without fully leveraging the unique properties of modality-specific features. To tackle this issue, researchers began exploring the connections between modality-specific features. [PERSON] et al. [32] proposed a novel deep neural network that learns discriminative cross-modal features, establishing a connection between the modalities of RGB and depth images. [PERSON] et al. [33] introduced the MMNet method, which consists of a multi-stage fusion module and a multi-scale decoder, enhancing RGB information with depth information to improve performance, but it neglects the important spatial features inherent in depth information. [PERSON] et al. [34] combined depth information with multi-scale contextual information and added an attention module to enhance model robustness. Additionally, several models have emerged that deeply explore the connections between modality-specific features, allowing the features of the two modalities to refine each other and enhance the model's generalization ability [9; 35]. However, this approach often results in significant data redundancy, making it crucial to reduce model complexity and make the model more lightweight. Consequently, [PERSON] et al. [36] designed a lightweight model suitable for mobile devices that relies solely on RGB images without utilizing depth information. Recently, [PERSON] et al. [37] proposed a novel network named EGA-Net aimed at improving edge quality and emphasizing the main features in RGB-D salient object detection. Thisnetwork innovatively addresses the issues of predicting structurally complete but blurred edges and effectively separating salient objects in complex backgrounds. [PERSON] et al. [38] were the first to propose the use of 3D convolutional neural networks for RGB-D salient object detection, which performs early fusion in the encoder stage and deep fusion in the decoder stage to effectively promote the comprehensive integration of RGB and depth streams. Additionally, [PERSON] et al.