One-Shot Image Segmentation with U-Net

Abstract

Image segmentation is an important task in the field of computer vision research. Many applications need accurate and efficient segmentation mechanisms. However, most existing methods need a large support set and have struggled with dealing with new classes in solving computer vision problems. In order to deal with the problem, this paper modified the traditional model to adapt it to the task of few-shot segmentation. Especially, the part of encoders was changed into a Siamese neural network for support branch and query branch. Then, the decoded query image is compared with different levels of features from the encoded support image. Experimental results show that this proposed model achieved the best performance compared with several baseline methods, and the improvement is more than 8%.