  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 and Local-Global Conditional Networks (CRCNet) for
few-shot segmentation. Unlike previous works that only predict the query
image's mask, our proposed model concurrently makes predictions for both the
support image and the query image. Our network can better find the co-occurrent
objects in the two images with a cross-reference mechanism, thus helping the
few-shot segmentation task. To further improve feature comparison, we develop a
local-global conditional module to capture both global and local relations. We
also develop a mask refinement module to refine the prediction of the
foreground regions recurrently. Experiments on the PASCAL VOC 2012, MS COCO,
and FSS-1000 datasets show that our network achieves new state-of-the-art
performance.
