  Existing RGB-D SOD methods mainly rely on a symmetric two-stream CNN-based
network to extract RGB and depth channel features separately. However, there
are two problems with the symmetric conventional network structure: first, the
ability of CNN in learning global contexts is limited; second, the symmetric
two-stream structure ignores the inherent differences between modalities. In
this paper, we propose a Transformer-based asymmetric network (TANet) to tackle
the issues mentioned above. We employ the powerful feature extraction
capability of Transformer (PVTv2) to extract global semantic information from
RGB data and design a lightweight CNN backbone (LWDepthNet) to extract spatial
structure information from depth data without pre-training. The asymmetric
hybrid encoder (AHE) effectively reduces the number of parameters in the model
while increasing speed without sacrificing performance. Then, we design a
cross-modal feature fusion module (CMFFM), which enhances and fuses RGB and
depth features with each other. Finally, we add edge prediction as an auxiliary
task and propose an edge enhancement module (EEM) to generate sharper contours.
Extensive experiments demonstrate that our method achieves superior performance
over 14 state-of-the-art RGB-D methods on six public datasets. Our code will be
released at https://github.com/lc012463/TANet.
