# Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects Source: [Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:57:11.341Z Authors: Md Amirul Islam, Mahmoud Kalash, Neil D. B. Bruce Published: 2018-03-14T00:20:28.000Z ## 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). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Camouflaged Object Ranking on CAM-FR (test)](): Camouflaged Object Ranking; CAM-FR (test); MAE: 0.074. - [Saliency Ranking on IRSR (test)](): Saliency Ranking; IRSR (test); SA-SOR: 0.46. - [Saliency Ranking on ASSR (test)](): Saliency Ranking; ASSR (test); SOR: 71.7. ## Original sources and implementation [Original paper]() [PDF]()