  The global and local contexts significantly contribute to the integrity of
predictions in Salient Object Detection (SOD). Unfortunately, existing methods
still struggle to generate complete predictions with fine details. There are
two major problems in conventional approaches: first, for global context,
high-level CNN-based encoder features cannot effectively catch long-range
dependencies, resulting in incomplete predictions. Second, downsampling the
ground truth to fit the size of predictions will introduce inaccuracy as the
ground truth details are lost during interpolation or pooling. Thus, in this
work, we developed a Transformer-based network and framed a supervised task for
a branch to learn the global context information explicitly. Besides, we adopt
Pixel Shuffle from Super-Resolution (SR) to reshape the predictions back to the
size of ground truth instead of the reverse. Thus details in the ground truth
are untouched. In addition, we developed a two-stage Context Refinement Module
(CRM) to fuse global context and automatically locate and refine the local
details in the predictions. The proposed network can guide and correct itself
based on the global and local context generated, thus is named, Self-Refined
Transformer (SelfReformer). Extensive experiments and evaluation results on
five benchmark datasets demonstrate the outstanding performance of the network,
and we achieved the state-of-the-art.
