  Over the past few years, state-of-the-art image segmentation algorithms are
based on deep convolutional neural networks. To render a deep network with the
ability to understand a concept, humans need to collect a large amount of
pixel-level annotated data to train the models, which is time-consuming and
tedious. Recently, few-shot segmentation is proposed to solve this problem.
Few-shot segmentation aims to learn a segmentation model that can be
generalized to novel classes with only a few training images. In this paper, we
propose a cross-reference network (CRNet) for few-shot segmentation. Unlike
previous works which only predict the mask in the query image, our proposed
model concurrently make predictions for both the support image and the query
image. With a cross-reference mechanism, our network can better find the
co-occurrent objects in the two images, thus helping the few-shot segmentation
task. We also develop a mask refinement module to recurrently refine the
prediction of the foreground regions. For the $k$-shot learning, we propose to
finetune parts of networks to take advantage of multiple labeled support
images. Experiments on the PASCAL VOC 2012 dataset show that our network
achieves state-of-the-art performance.
