  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 for few-shot segmentation, the task-adaptive feature
transformer (TAFT). TAFT linearly transforms task-specific high-level features
to a set of task-agnostic features well-suited to the segmentation job. Using
this task-conditioned feature transformation, the model is shown to effectively
utilize the semantic information in novel classes to generate tight
segmentation masks. The proposed TAFT module can be easily plugged into
existing semantic segmentation algorithms to achieve few-shot segmentation
capability with only a few added parameters. We combine TAFT with Deeplab V3+,
a well-known segmentation architecture; experiments on the PASCAL-$5^i$ dataset
confirm that this combination successfully adds few-shot learning capability to
the segmentation algorithm, achieving the state-of-the-art few-shot
segmentation performance in some key representative cases.
