Joint Coarse-and-Fine Semantic Segmentation

Abstract

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.