  Few-shot learning allows machines to classify novel classes using only a few
labeled samples. Recently, few-shot segmentation aiming at semantic
segmentation on low sample data has also seen great interest. In this paper, we
propose a learnable module that can be placed on top of existing segmentation
networks for performing few-shot segmentation. This module, called the
task-adaptive feature transformer (TAFT), linearly transforms task-specific
high-level features to a set of task agnostic features well-suited to
conducting few-shot segmentation. The task-conditioned feature transformation
allows an effective utilization of the semantic information in novel classes to
generate tight segmentation masks. We also propose a semantic enrichment (SE)
module that utilizes a pixel-wise attention module for high-level feature and
an auxiliary loss from an auxiliary segmentation network conducting the
semantic segmentation for all training classes. Experiments on PASCAL-$5^i$ and
COCO-$20^i$ datasets confirm that the added modules successfully extend the
capability of existing segmentators to yield highly competitive few-shot
segmentation performances.
