  Few-shot instance segmentation methods are promising when labeled training
data for novel classes is scarce. However, current approaches do not facilitate
flexible addition of novel classes. They also require that examples of each
class are provided at train and test time, which is memory intensive. In this
paper, we address these limitations by presenting the first incremental
approach to few-shot instance segmentation: iMTFA. We learn discriminative
embeddings for object instances that are merged into class representatives.
Storing embedding vectors rather than images effectively solves the memory
overhead problem. We match these class embeddings at the RoI-level using cosine
similarity. This allows us to add new classes without the need for further
training or access to previous training data. In a series of experiments, we
consistently outperform the current state-of-the-art. Moreover, the reduced
memory requirements allow us to evaluate, for the first time, few-shot instance
segmentation performance on all classes in COCO jointly.
