  The saliency ranking task is recently proposed to study the visual behavior
that humans would typically shift their attention over different objects of a
scene based on their degrees of saliency. Existing approaches focus on learning
either object-object or object-scene relations. Such a strategy follows the
idea of object-based attention in Psychology, but it tends to favor those
objects with strong semantics (e.g., humans), resulting in unrealistic saliency
ranking. We observe that spatial attention works concurrently with object-based
attention in the human visual recognition system. During the recognition
process, the human spatial attention mechanism would move, engage, and
disengage from region to region (i.e., context to context). This inspires us to
model the region-level interactions, in addition to the object-level reasoning,
for saliency ranking. To this end, we propose a novel bi-directional method to
unify spatial attention and object-based attention for saliency ranking. Our
model includes two novel modules: (1) a selective object saliency (SOS) module
that models objectbased attention via inferring the semantic representation of
the salient object, and (2) an object-context-object relation (OCOR) module
that allocates saliency ranks to objects by jointly modeling the object-context
and context-object interactions of the salient objects. Extensive experiments
show that our approach outperforms existing state-of-theart methods. Our code
and pretrained model are available at https://github.com/GrassBro/OCOR.
