Self-Attention Recurrent Network for Saliency Detection
This work addresses saliency detection for computer vision applications, but it is incremental as it builds on existing deep learning methods with specific enhancements.
The paper tackled the problem of sub-optimal saliency detection by proposing a deep network that enhances local and global information from multi-scale feature maps, achieving better performance over existing algorithms on four datasets.
Feature maps in deep neural network generally contain different semantics. Existing methods often omit their characteristics that may lead to sub-optimal results. In this paper, we propose a novel end-to-end deep saliency network which could effectively utilize multi-scale feature maps according to their characteristics. Shallow layers often contain more local information, and deep layers have advantages in global semantics. Therefore, the network generates elaborate saliency maps by enhancing local and global information of feature maps in different layers. On one hand, local information of shallow layers is enhanced by a recurrent structure which shared convolution kernel at different time steps. On the other hand, global information of deep layers is utilized by a self-attention module, which generates different attention weights for salient objects and backgrounds thus achieve better performance. Experimental results on four widely used datasets demonstrate that our method has advantages in performance over existing algorithms.