  Salient object detection aims at detecting the most visually distinct objects
and producing the corresponding masks. As the cost of pixel-level annotations
is high, image tags are usually used as weak supervisions. However, an image
tag can only be used to annotate one class of objects. In this paper, we
introduce saliency subitizing as the weak supervision since it is
class-agnostic. This allows the supervision to be aligned with the property of
saliency detection, where the salient objects of an image could be from more
than one class. To this end, we propose a model with two modules, Saliency
Subitizing Module (SSM) and Saliency Updating Module (SUM). While SSM learns to
generate the initial saliency masks using the subitizing information, without
the need for any unsupervised methods or some random seeds, SUM helps
iteratively refine the generated saliency masks. We conduct extensive
experiments on five benchmark datasets. The experimental results show that our
method outperforms other weakly-supervised methods and even performs comparably
to some fully-supervised methods.
