  Most of existing methods for few-shot object detection follow the fine-tuning
paradigm, which potentially assumes that the class-agnostic generalizable
knowledge can be learned and transferred implicitly from base classes with
abundant samples to novel classes with limited samples via such a two-stage
training strategy. However, it is not necessarily true since the object
detector can hardly distinguish between class-agnostic knowledge and
class-specific knowledge automatically without explicit modeling. In this work
we propose to learn three types of class-agnostic commonalities between base
and novel classes explicitly: recognition-related semantic commonalities,
localization-related semantic commonalities and distribution commonalities. We
design a unified distillation framework based on a memory bank, which is able
to perform distillation of all three types of commonalities jointly and
efficiently. Extensive experiments demonstrate that our method can be readily
integrated into most of existing fine-tuning based methods and consistently
improve the performance by a large margin.
