  We tackle the challenging task of few-shot segmentation in this work. It is
essential for few-shot semantic segmentation to fully utilize the support
information. Previous methods typically adopt masked average pooling over the
support feature to extract the support clues as a global vector, usually
dominated by the salient part and lost certain essential clues. In this work,
we argue that every support pixel's information is desired to be transferred to
all query pixels and propose a Correspondence Matching Network (CMNet) with an
Optimal Transport Matching module to mine out the correspondence between the
query and support images. Besides, it is critical to fully utilize both local
and global information from the annotated support images. To this end, we
propose a Message Flow module to propagate the message along the inner-flow
inside the same image and cross-flow between support and query images, which
greatly helps enhance the local feature representations. Experiments on PASCAL
VOC 2012, MS COCO, and FSS-1000 datasets show that our network achieves new
state-of-the-art few-shot segmentation performance.
