  Salient object detection is the pixel-level dense prediction task which can
highlight the prominent object in the scene. Recently U-Net framework is widely
used, and continuous convolution and pooling operations generate multi-level
features which are complementary with each other. In view of the more
contribution of high-level features for the performance, we propose a triplet
transformer embedding module to enhance them by learning long-range
dependencies across layers. It is the first to use three transformer encoders
with shared weights to enhance multi-level features. By further designing scale
adjustment module to process the input, devising three-stream decoder to
process the output and attaching depth features to color features for the
multi-modal fusion, the proposed triplet transformer embedding network
(TriTransNet) achieves the state-of-the-art performance in RGB-D salient object
detection, and pushes the performance to a new level. Experimental results
demonstrate the effectiveness of the proposed modules and the competition of
TriTransNet.
