# Hybrid-Attention Network for RGB-D Salient Object Detection
## 1 Introduction  
Saliency detection extracts relevant objects with pixel-level details from an image. It has been widely used in many fields such as object segmentation [1], region proposal [2], object recognition [3], image quality assessment [4], and video analysis [5]. It has been found that when the background has similar colors to those of a salient object or it is highly complex and salient objects are very large or small, saliency detection solely based on RGB images often fails to provide accurate results. Therefore, depth information is being increasingly used as a supplement to RGB information for saliency detection [6; 7; 8]. RGB-D salient object detection based on handcrafted features generally uses depth maps to determine edges, textures, and histogram statistics, and then bottom-up [9] or top-down [10] approaches are used to predict whether a pixel belongs to a salient object. Various methods consider the rarity of pixels in an image at local and global regions [11], while others use prior knowledge to support predictionand obtain accurate detection [12]. However, these methods rely on handcrafted features, empirical parameter setting, and statistical prediction, which limit their performance. In fact, such methods cannot fully extract representative features due to inadequate parameter setting, subjective factors, and redundant or erroneous information. In addition, models of the human visual system may be incomplete and misleading. Alternatively, deep learning methods have emerged in recent years, improving the accuracy of salient object detection [13; 14; 15; 16]. By combining the advantages of deep learning and features in depth maps, several stereoscopic saliency detection methods based on neural networks have achieved great leaps in accuracy. For instance, DF combines RGB images and depth maps into a deep learning framework [17]. Then, encoder-decoder networks, such as PDNet [18], provide high accuracy and robustness. [PERSON] et al. further improved the results by proposing hidden structure conversion [19], complementary fusion [20], a dilated convolutional model [21], and modification to loss functions [22] for highly accurate salient object detection. On the other hand, methods based on attention mechanisms can quickly identify the position of objects and then reconstruct the edges for improving salient object detection. [PERSON] et al. proposed a residual network with attention mechanism [23] and then DANet [24] to achieve accurate results by using channel and spatial attention maps. Current stereoscopic salient object detection based on deep learning usually adopts networks such as VGG [25], ResNet [26], and Inception [27] as its backbone and the U-Net encoding-decoding structure [28] as the framework. However, this is not an ideal solution for saliency detection. As the depth map (disparity map) is an image reflecting the distances to objects, many networks use it to generate an attention map to distinguish objects from the background. However, depth maps have two major limitations. First, the depth map reflects the distance to all objects, and some non-salient objects are the closest to camera and provide the lowest (highest) pixel intensities. Thus, the underlying network may consider such objects as salient, in a phenomenon that we call the depth principle error. Second, data acquisition limitations may degrade the accuracy of edge information in the depth map. Overall, the neural networks that determine the location of objects using only depth information to construct the attention map may be biased. Using the RGB image to discard the closest non-salient objects in depth maps may improve the detection accuracy. Based on spatial attention maps, we propose stereoscopic salient object detection using a hybrid attention network (HANet). Before processing features for saliency detection, high-level features extracted from the RGB image are encoded into an attention map, which is then mixed with the depth attention map for subsequent joint processing with the saliency features.