  Saliency detection is one of the basic challenges in computer vision. How to
extract effective features is a critical point for saliency detection. Recent
methods mainly adopt integrating multi-scale convolutional features
indiscriminately. However, not all features are useful for saliency detection
and some even cause interferences. To solve this problem, we propose Pyramid
Feature Attention network to focus on effective high-level context features and
low-level spatial structural features. First, we design Context-aware Pyramid
Feature Extraction (CPFE) module for multi-scale high-level feature maps to
capture rich context features. Second, we adopt channel-wise attention (CA)
after CPFE feature maps and spatial attention (SA) after low-level feature
maps, then fuse outputs of CA & SA together. Finally, we propose an edge
preservation loss to guide network to learn more detailed information in
boundary localization. Extensive evaluations on five benchmark datasets
demonstrate that the proposed method outperforms the state-of-the-art
approaches under different evaluation metrics.
