Top-Down Target Object Detection Through Context

Visual attention is crucial for identifying the most salient regions in an image. However, when the objective involves higher-level visual tasks, such as target object detection, it becomes necessary to incorporate high-level information to guide the search for the target. This process, known as top-down saliency detection, leverages guidance sources like contextual information and target features to identify regions of interest that are more likely to contain the target object. In this paper, we propose a model that generates top-down saliency maps by adjusting the feature map weights of a universal visual attention model based on contextual information. While contextual information has traditionally been used to understand the gist of an image, it has not been integrated into the creation of saliency maps for target object detection. We demonstrate that incorporating contextual information into a visual attention model enhances target object detection performance. The proposed model, tested on six datasets, shows significant improvements in detecting target objects compared to models that do not utilize contextual information.