Joint Coarse-and-Fine Semantic Segmentation

The issue of image semantic segmentation is renowned within computer vision and artificial intelligence. The ground truth in image segmentation is hard to produce and is time- and resource-intensive. Recent research on realtime image semantic segmentation based on deep learning has reduced image resolution through pooling operations, resulting in detail loss in the scene. In order to generate high-quality annotated data, in this paper, we propose a joint coarse-and-fine (JCF) architecture that can repair fragment defects based on a coarse module, and also produce fine details based on a fine module. The experiments show promising results compared to state-of-the-art methods.