Saliency Detection With a Three-Stage Hierarchical Network

Abstract

Deep learning approaches for saliency detection have attracted much attention and have been exploited widely in recent years. In this article, we propose a three-stage hierarchical neural network for modeling the detection. Initially, fast R-CNN is used to extract features for each superpixel, and the high-level prior information of traditional models is incorporated to weight the deep learning features. Next, in the regional stage, a self-attention mechanism is used to expand the receptive field from one superpixel to its surrounding and relevant regions. And last, saliency scores are sampled by the Gumbel–Softmax trick in a global regression model. In the experiments, we compare our models including two variations (networks without self-attention or prior weights) with 12 previous methods and test them on several benchmark datasets. Different kinds of strategies are also adopted for evaluation and the results demonstrate that our method achieves excellent performance.