Category-based depth incorporation for salient object ranking

Abstract

Salient object ranking is a visual task that aims to mimic the human visual system’s ability to prioritize various salient objects in complex scenes. It integrates various computer vision tasks, including object detection and instance segmentation, and draws insights from psychology and biology. Despite recent advancements in salient object ranking research, there is still a need for a more in-depth exploration of certain underlying factors that influence the ranking. Previous studies have primarily focused on factors such as the scale and position of objects, as well as interactions between objects and their context. However, they often overlooked the crucial interaction between objects and observers. Unlike other visual tasks, salient object ranking is highly subjective and influenced by the observer. Therefore, establishing a meaningful connection between the observed object and the observer becomes crucial. Our model addresses this gap by placing emphasis on the distance between objects and observers. It investigates the implicit relationship between object categories and distance through the Category-Aware Attention (CAA) module. This innovative approach incorporates depth into salient object ranking, resulting in an improvement in ranking performance.