Revisiting Salient Object Detection:
Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects
Abstract
Salient object detection is a problem that has been considered in detail and many solutions proposed. In this paper,
we argue that work to date has addressed a problem that is
relatively ill-posed. Specifically, there is not universal agreement about what constitutes a salient object when multiple
observers are queried. This implies that some objects are
more likely to be judged salient than others, and implies a
relative rank exists on salient objects. The solution presented
in this paper solves this more general problem that considers
relative rank, and we propose data and metrics suitable to
measuring success in a relative object saliency landscape.
A novel deep learning solution is proposed based on a hierarchical representation of relative saliency and stage-wise
refinement. We also show that the problem of salient object
subitizing can be addressed with the same network, and our
approach exceeds performance of any prior work across all
metrics considered (both traditional and newly proposed)