Global Context Encoding for Salient Objects Detection
Jingbo Wang, Yajie Xing, Gang Zeng · 2018
Deep convolutional neural networks (CNNs) have gained their reputation for the success in various tasks in computer vision, including salient objects detection. However, it remains a challenge that the CNNs have repeated downsample operators and always create low-resolution predictions, which tend to loss details and finer structure of images. To detect and segment the salient objects well, it is also necessary to merge high-level semantic information and low-level fine details simultaneously. Thus, we propose a novel network structure with stage-wise refinement sub-structures. In addition, we exploit the essence of salient objects detection by encoding the global image context in a specifically designed module, which is applied to every stage of the refinement structure. So the coarse saliency map generated from the base CNN can be refined with low-level feature and global context information step-by-step. Experimental results have demonstrated that the proposed method outperforms the state-of-the-art approaches on four benchmark datasets.