Multi-Path Feature Fusion Network for Saliency Detection

Recent saliency detection methods have made great progress with the fully convolutional network. However, we find that the saliency maps are usually coarse and fuzzy, especially near the boundary of salient object. To deal with this problem, in this paper, we exploit a multi-path feature fusion model for saliency detection. The proposed model is a fully convolutional network with raw images as input and saliency maps as output. In particular, we propose a multi-path fusion strategy for deriving the intrinsic features of salient objects. The structure has the ability of capturing the low-level visual features and generating the boundary-preserving saliency maps. Moreover, a coupled structure module is proposed in our model, which helps to explore the high-level semantic properties of salient objects. Extensive experiments on four public benchmarks indicate that our saliency model is effective and outperforms state-of-the-art methods.