  RGB-D SOD uses depth information to handle challenging scenes and obtain
high-quality saliency maps. Existing state-of-the-art RGB-D saliency detection
methods overwhelmingly rely on the strategy of directly fusing depth
information. Although these methods improve the accuracy of saliency prediction
through various cross-modality fusion strategies, misinformation provided by
some poor-quality depth images can affect the saliency prediction result. To
address this issue, a novel RGB-D salient object detection model (SiaTrans) is
proposed in this paper, which allows training on depth image quality
classification at the same time as training on SOD. In light of the common
information between RGB and depth images on salient objects, SiaTrans uses a
Siamese transformer network with shared weight parameters as the encoder and
extracts RGB and depth features concatenated on the batch dimension, saving
space resources without compromising performance. SiaTrans uses the Class token
in the backbone network (T2T-ViT) to classify the quality of depth images
without preventing the token sequence from going on with the saliency detection
task. Transformer-based cross-modality fusion module (CMF) can effectively fuse
RGB and depth information. And in the testing process, CMF can choose to fuse
cross-modality information or enhance RGB information according to the quality
classification signal of the depth image. The greatest benefit of our designed
CMF and decoder is that they maintain the consistency of RGB and RGB-D
information decoding: SiaTrans decodes RGB-D or RGB information under the same
model parameters according to the classification signal during testing.
Comprehensive experiments on nine RGB-D SOD benchmark datasets show that
SiaTrans has the best overall performance and the least computation compared
with recent state-of-the-art methods.
