Bi-Directional Selectivity Refinement Network for Salient Object Detection
Junbin Yuan, Peiting Li, Qinzhen Xu, Zhenyang Lin, Xueying Lin, Yongyi Gong · 2020
Salient object detection is a fundamental computer vision task. The majority of current algorithms focus on the use of edge information. However, these algorithms are limited to one-way auxiliary training or feature fusion to improve salient features. In this paper, we propose a novel framework for salient object detection, called Bi-Directional Selectivity Refinement Network (BSRN). Our framework aims to simultaneously refine salient features and perfect edge features through a Bi-directional Selectivity Refinement Module (BSRM). In this process, we innovatively combine short connections and attention mechainsm to fully use multi-scale features, selectively enhance features, reduce redundancy and suppress distractors. Besides, in order to improve the adaptability to detect multi-scale objects and achieve better result, we also propose a Multi-scale Feature Extraction Module (MFEM) to capture global contextual information. Extensive experiments conducted on four benchmark datasets demonstrate that our method outperforms 10 similar methods.