关键词: 显著性检测, 全卷积网络, 特征引导, 多尺度和多层次特征, 残差结构

Abstract:

Recently, saliency detection methods based on Fully Convolutional Network（FCN） have made great progress compared with the methods using handcrafted feature, however, there are still some problems to be solved in images with complex scenes. This paper proposes a novel feature-guided salient object detection model through investigating the important role of deep features in convolutional neural network. Based on the decode-encode architecture of feature pyramid network, firstly, it designs a Global Guidance Module（GGM） upon the bottom-up pathway, to extract the location information of salient objects. And then, it further builds a contextual Residual Module（RM） to extract more detail information at different output sides. Moreover, it employs Guiding Flows（GF） to aggregate the features generated by GGM and RM. The proposed approach uses deep semantics to guide the feature learning in shallower layers, which makes the network focus on the salient object and ignore the irrelevant background. Experimental results show that the proposed model improves the performance compared to the state-of-the-art methods on five benchmark datasets.

Key words: saliency detection, fully convolutional network, feature guidance, multi-scale and multi-level feature, residual structure