Hierarchical U-Shape Attention Network for Salient Object Detection

Salient object detection aims at locating the most conspicuous objects in natural images, which usually acts as a very important pre-processing procedure in many computer vision tasks. In this paper, we propose a simple yet effective Hierarchical U-shape Attention Network (HUAN) to learn a robust mapping function for salient object detection. Firstly, a novel attention mechanism is formulated to improve the well-known U-shape network, in which the memory consumption can be extensively reduced and the mask quality can be significantly improved by the resulting U-shape Attention Network (UAN). Secondly, a novel hierarchical structure is constructed to well bridge the low-level and high-level feature representations between different UANs, in which both the intra-network and inter-network connections are considered to explore the salient patterns from a local to global view. Thirdly, a novel Mask Fusion Network (MFN) is designed to fuse the intermediate prediction results, so as to generate a salient mask which is in higher-quality than any of those inputs. Our HUAN can be trained together with any backbone network in an end-to-end manner, and high-quality masks can be finally learned to represent the salient objects. Extensive experimental results on several benchmark datasets show that our method significantly outperforms most of the state-of-the-art approaches.