  Few-shot segmentation segments object regions of new classes with a few of
manual annotations. Its key step is to establish the transformation module
between support images (annotated images) and query images (unlabeled images),
so that the segmentation cues of support images can guide the segmentation of
query images. The existing methods form transformation model based on global
cues, which however ignores the local cues that are verified in this paper to
be very important for the transformation. This paper proposes a new
transformation module based on local cues, where the relationship of the local
features is used for transformation. To enhance the generalization performance
of the network, the relationship matrix is calculated in a high-dimensional
metric embedding space based on cosine distance. In addition, to handle the
challenging mapping problem from the low-level local relationships to
high-level semantic cues, we propose to apply generalized inverse matrix of the
annotation matrix of support images to transform the relationship matrix
linearly, which is non-parametric and class-agnostic. The result by the matrix
transformation can be regarded as an attention map with high-level semantic
cues, based on which a transformation module can be built simply.The proposed
transformation module is a general module that can be used to replace the
transformation module in the existing few-shot segmentation frameworks. We
verify the effectiveness of the proposed method on Pascal VOC 2012 dataset. The
value of mIoU achieves at 57.0% in 1-shot and 60.6% in 5-shot, which
outperforms the state-of-the-art method by 1.6% and 3.5%, respectively.
