Human-in-the-loop image segmentation and annotation

Dear editor, Semantic segmentation aims to assign the category information to all pixels of an image and plays a vital role in image understanding. In the past few years, deep convolutional neural networks have achieved great success in a large variety of computer vision tasks. Inspired by the advances of CNN in recognition, a fully convolutional network (FCN) is developed in an end-to-end, pixel-to-pixel training manner for semantic segmentation. Owing to the computational efficiency for dense prediction and end-to-end learning manner, numerous variants of FCN are then proposed to boost the performance of semantic segmentation. The excellent performance of deep models, however, highly relies on expensive and laborious label annotations of massive images [1]. Actually, most existing deep learning models for semantic segmentation are firstly pre-trained on millions of images with sample-level annotations …