# Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
## 2 Related Work
### RGB Salient Object Detection  
In the early stages of salient object detection, researchers mainly relied on hand-designed features and heuristics. [PERSON] et al. [20] proposed a biologically inspired saliency detection model in 1998, which uses features such as color, intensity, and orientation of images to calculate saliency maps through multi-scale feature fusion. This method is simple and intuitive and simulates the attention perception of the human eye, but it often performs poorly when processing complex scenes.  
As a result, researchers developed image contrast and saliency map generation methods. [PERSON] et al. [21] proposed a saliency detection method based on global contrast in 2009, which generates saliency maps by calculating the global contrast of each pixel in the image. [PERSON] et al. [22] proposed the regional contrast method in 2014, which generates saliency maps by segmenting the image into superpixel regions and calculating the contrast between regions. These methods improve the performance of saliency detection to a certain extent but still rely on hand-designed features and heuristic rules.  
After entering the era of deep learning, most people choose convolutional neural networks as the main salient target detection method. Deep learning methods use an end-to-end framework to automatically extract high-level features from massive amounts of information, thereby greatly improving the accuracy of saliency detection. [PERSON] et al. [23] proposed the DeepSaliency model in 2016, which uses a multi-task deep neural network to learn saliency detection and related tasks simultaneously, thereby improving detection performance. In recent years, researchers have continued to explore more efficient deep learning models and network architectures to further enable salient object detection with higher accuracy. RGB salient object detection continues to make significant progress in model architecture and training strategies. [PERSON] et al. [24] proposed an Integrity Consensus Network (ICON) in 2023, aiming to improve integrity learning for salient object detection, which effectively improved the accuracy and robustness of salient object detection at both micro and macro levels. [PERSON] et al. [25] proposed a cross-modal coordinate attention network (CCAFusion) in 2024 for the fusion of infrared and visible light images. Through feature awareness fusion and feature enhancement fusion modules, combined with methods based on multi-scale skip connections, effective information fusion is achieved. [PERSON] et al. [26] proposed a hierarchical attention-related context-driven network (RCNet) for salient object detection in 2024. Through multi-receptive field dilated convolutions and multi-source mixed channel attention mechanisms in a diamond hierarchical structure, RCNet effectively improves the accuracy and robustness of feature representation. Although RGB-based salient object detection methods perform well in many applications, they still present challenges when dealing with complex backgrounds, occlusions, and low-contrast targets. Relying only on the information of RGB images, it is difficult to fully distinguish the foreground and background in some cases, thus affecting detection performance.