  The transformer networks, which originate from machine translation, are
particularly good at modeling long-range dependencies within a long sequence.
Currently, the transformer networks are making revolutionary progress in
various vision tasks ranging from high-level classification tasks to low-level
dense prediction tasks. In this paper, we conduct research on applying the
transformer networks for salient object detection (SOD). Specifically, we adopt
the dense transformer backbone for fully supervised RGB image based SOD, RGB-D
image pair based SOD, and weakly supervised SOD via scribble supervision. As an
extension, we also apply our fully supervised model to the task of camouflaged
object detection (COD) for camouflaged object segmentation. For the fully
supervised models, we define the dense transformer backbone as feature encoder,
and design a very simple decoder to produce a one channel saliency map (or
camouflage map for the COD task). For the weakly supervised model, as there
exists no structure information in the scribble annotation, we first adopt the
recent proposed Gated-CRF loss to effectively model the pair-wise relationships
for accurate model prediction. Then, we introduce self-supervised learning
strategy to push the model to produce scale-invariant predictions, which is
proven effective for weakly supervised models and models trained on small
training datasets. Extensive experimental results on various SOD and COD tasks
(fully supervised RGB image based SOD, fully supervised RGB-D image pair based
SOD, weakly supervised SOD via scribble supervision, and fully supervised RGB
image based COD) illustrate that transformer networks can transform salient
object detection and camouflaged object detection, leading to new benchmarks
for each related task.
