# Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
###### Abstract
Specifically, two independent network branches are used to extract features, and then the two features are fused at the end of the encoder; finally, the saliency result map is output through the decoder. Compared with early fusion, late fusion can effectively improve detection accuracy. (c) represents one type of mid-term fusion, which is also the method we use. By adding a new fusion branch in addition to the original RGB branch and depth branch, deeper processing of RGB features and depth features can be performed, which has strong complementarity. in an interactive and adaptive manner, distinguishing the information from two different modalities, and using multiple levels of deep features to express the RGB features in more detail. [PERSON] et al. [14] proposed a selective self-mutual attention module (S2 MA) to fuse the features learned in the two modalities. Additionally, several methods assist in the task of enhancing RGB features for saliency detection through features extracted from depth images. [PERSON] et al. [15] processed depth images by using an auxiliary sub-network, encoding the resulting depth image features, concatenating them with the RGB image in the channel direction, and finally obtaining the final saliency map through operations such as upsampling. [PERSON] et al. [16] aimed to create a high-accuracy detection network, so they designed a deep feature extraction network to separately extract depth image information and proposed an alternating correction strategy in the decoding stage, in which depth features are gradually added to the decoding process for prediction. Although the above models have made undeniable progress and improved the accuracy of RGB-D saliency detection tasks, there are still many obvious problems that need to be solved. First, the above methods generally use depth information as an auxiliary cue to refine the RGB features of the SOD task while ignoring the attribute dependence between the two modalities. Furthermore, the above methods mainly extract shared features by fusing the single-modal feature representations of RGB images and depth images, and finally, the final predicted saliency map is obtained through the decoder network. This approach focuses excessively on the fusion of cross-modal information, neglecting the complementary properties between RGB images and depth images at different stages. Moreover, if the RGB-D cross-modal information is simply concatenated, spliced, or added during the fusion process, the truly effective information may be submerged in the redundancy of a large amount of data. To address the above problems, we propose a cross-modal adaptive interaction network (CMANet). This network follows an encoder-decoder architecture. In the encoder part, we first use the Res2 Net-50 [17] network with the PSA polarization attention mechanism [18] to extract multi-scale features from RGB images and depth images. This combination not only retains the multi-scale feature extraction capability of Res2 Net-50 [17] but also utilizes PSA to enhance the global contextual information of the features. We propose a cross-modal feature integration module (CMF) to fully integrate the multi-scale features of RGB and depth images. In the decoder part, we employ the U-Net network structure [19], which can directly transfer the high-resolution features from the encoder to the corresponding layers of the decoder, aiding in the restoration of spatial information and details. To fuse feature maps of different scales into comprehensive full-scale feature maps and improve the model's multi-scale perception ability, we propose an adaptive feature fusion module (AFFM) to integrate these features within the decoder. Based on the above structure, the model can capture global information while retaining the details and boundary information of the image, thereby generating high-quality output results. As shown in Figure 2, this is a comparison chart of our model with other models. This paper mainly proposes the following contributions:  
1.