Bi-directional Object-context Prioritization Learning for Saliency Ranking

TL;DR

This paper introduces a bi-directional model for saliency ranking that combines object-based and spatial attention mechanisms, inspired by human visual recognition, to improve the accuracy of identifying and ranking salient objects in scenes.

Contribution

It proposes a novel bi-directional framework with SOS and OCOR modules to unify spatial and object-based attention for more realistic saliency ranking.

Findings

Outperforms existing state-of-the-art methods.

Effectively models object-context interactions.

Enhances saliency ranking accuracy.

Abstract

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.