# Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
###### Abstract  
The salient object detection (SOD) task aims to automatically detect the most prominent areas observed by the human eye in an image. Since RGB images and depth images contain different information, how to effectively integrate cross-modal features in the RGB-D SOD task remains a major challenge. Therefore, this paper proposes a cross-modal adaptive interaction network (CMANet) for the RGB-D salient object detection task, which consists of a cross-modal feature integration module (CMF) and an adaptive feature fusion module (AFFM). These modules are designed to integrate and enhance multi-scale features from both modalities, improve the effect of integrating cross-modal complementary information of RGB and depth images, enhance feature information, and generate richer and more representative feature maps. Extensive experiments were conducted on four RGB-D datasets to verify the effectiveness of CMANet. Compared with 17 RGB-D SOD methods, our model accurately detects salient regions in images and achieves state-of-the-art performance across four evaluation metrics. salient object detection; RGB-D images; cross-modality interaction +
Footnote †: journal:abilities of the human eye and enhance the computer's recognition of targets. This has aroused researchers' interest in developing the RGB-D SOD method. Many experiments have verified that using RGB-D images for salient object detection tasks can achieve better detection results in many difficult environments [6]. However, how to fully explore the correlation properties between the two modalities and fully utilize their respective advantages to integrate RGB and depth information is still an issue that needs attention [7; 8]. As a prediction task at the pixel level, RGB-D salient object detection based on deep learning mostly adopts an encoder-decoder network architecture. The encoder part primarily uses a pre-trained convolutional neural network model to map the input image to the hidden layer to extract multi-level feature representations; then, the decoder is used to restore the image resolution and finally generate the saliency map detection results. Currently, researchers can divide the fusion methods into three types according to the different positions of RGB images and depth images in the network: early fusion, mid-term fusion, and late fusion [9]. As shown in Figure 1, specifically, early fusion occurs at the input stage; for the RGB image and the depth image, the two are only connected at the channel angle, forming a four-channel image as input, followed by subsequent encoder and decoder operations [10]. However, this fusion method does not consider the inherent differences between RGB images and depth images, which may lead to inaccurate detection results. Late fusion refers to using two independent network models to detect the saliency maps of RGB and depth images, respectively, and then simply multiplying the two saliency maps or obtaining their sum as the final result [11]. This method loses a lot of deep information in the image and cannot exploit the intrinsic relationships between RGB images and depth images, often resulting in blurred edges or insufficiently predicted regions in the resulting image [12]. To address these issues, researchers have explored the mid-term fusion strategy. Mid-term fusion mainly investigates how to fuse multi-level features to extract complementary information, thereby achieving a balance between accuracy and efficiency. [PERSON] et al. [13] proposed ICNet to mine the complementary relationship between two modalities and the coherence between layers, fusing advanced RGB and depth features  
Figure 1: (a) shows early fusion. Specifically, the RGB image and the depth image first undergo feature extraction and are then sent to the encoder–decoder network to integrate the features (usually by simply concatenating their representations) and finally predict the saliency result map. (b) represents late fusion.