  Salient object detection on RGB-D images is an active topic in computer
vision. Although the existing methods have achieved appreciable performance,
there are still some challenges. The locality of convolutional neural network
requires that the model has a sufficiently deep global receptive field, which
always leads to the loss of local details. To address the challenge, we propose
a novel Group Transformer Network (GroupTransNet) for RGB-D salient object
detection. This method is good at learning the long-range dependencies of cross
layer features to promote more perfect feature expression. At the beginning,
the features of the slightly higher classes of the middle three levels and the
latter three levels are soft grouped to absorb the advantages of the high-level
features. The input features are repeatedly purified and enhanced by the
attention mechanism to purify the cross modal features of color modal and depth
modal. The features of the intermediate process are first fused by the features
of different layers, and then processed by several transformers in multiple
groups, which not only makes the size of the features of each scale unified and
interrelated, but also achieves the effect of sharing the weight of the
features within the group. The output features in different groups complete the
clustering staggered by two owing to the level difference, and combine with the
low-level features. Extensive experiments demonstrate that GroupTransNet
outperforms the comparison models and achieves the new state-of-the-art
performance.
