  RGB-D salient object detection (SOD) has been in the spotlight recently
because it is an important preprocessing operation for various vision tasks.
However, despite advances in deep learning-based methods, RGB-D SOD is still
challenging due to the large domain gap between an RGB image and the depth map
and low-quality depth maps. To solve this problem, we propose a novel
superpixel prototype sampling network (SPSN) architecture. The proposed model
splits the input RGB image and depth map into component superpixels to generate
component prototypes. We design a prototype sampling network so that the
network only samples prototypes corresponding to salient objects. In addition,
we propose a reliance selection module to recognize the quality of each RGB and
depth feature map and adaptively weight them in proportion to their
reliability. The proposed method makes the model robust to inconsistencies
between RGB images and depth maps and eliminates the influence of non-salient
objects. Our method is evaluated on five popular datasets, achieving
state-of-the-art performance. We prove the effectiveness of the proposed method
through comparative experiments.
