  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).

Computer Vision and Pattern Recognition