Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection

With the development of convolutional neural networks (CNNs), saliency detection methods have made a big progress in recent years. However, the previous methods sometimes mistakenly highlight the non-salient region, especially in complex backgrounds. To solve this problem, a two-stage method for saliency detection is proposed in this paper. In the first stage, a network is used to regress the minimum salient region (RMSR) containing all salient objects. Then in the second stage, in order to fuse the multi-level features, the spiral sharing network (SSN) is proposed for pixel-level detection on the result of RMSR. Experimental results on four public datasets show that our model is effective over the state-of-the-art approaches.