  Both generalized and incremental few-shot learning have to deal with three
major challenges: learning novel classes from only few samples per class,
preventing catastrophic forgetting of base classes, and classifier calibration
across novel and base classes. In this work we propose a three-stage framework
that allows to explicitly and effectively address these challenges. While the
first phase learns base classes with many samples, the second phase learns a
calibrated classifier for novel classes from few samples while also preventing
catastrophic forgetting. In the final phase, calibration is achieved across all
classes. We evaluate the proposed framework on four challenging benchmark
datasets for image and video few-shot classification and obtain
state-of-the-art results for both generalized and incremental few shot
learning.
