DHNet: Salient Object Detection With Dynamic Scale-Aware Learning and Hard-Sample Refinement

During the annotation procedure of salient object detection, researchers usually locate the approximate location of the salient objects first and then process the pixels that need to be finely annotated. Following this idea, we find that the existing methods have limited exploration for solving the problem of positioning salient objects. Furthermore, no effective solution has been proposed for the hard-sample problem related to this task. Therefore, we propose dynamic scale-aware learning to learn dynamic scale weights that vary with different images to solve the first problem. Second, we design a dense sampling strategy for hard samples to construct a graph representation with samples from different classes and different confidence levels. Then, we achieve targeted feature aggregation based on the constructed graph with the help of the graph attention mechanism. We conduct extensive experiments on five benchmark datasets using comprehensive evaluation metrics. The results show that our method outperforms the current state-of-the-art approaches.