  Conventional training of a deep CNN based object detector demands a large
number of bounding box annotations, which may be unavailable for rare
categories. In this work we develop a few-shot object detector that can learn
to detect novel objects from only a few annotated examples. Our proposed model
leverages fully labeled base classes and quickly adapts to novel classes, using
a meta feature learner and a reweighting module within a one-stage detection
architecture. The feature learner extracts meta features that are generalizable
to detect novel object classes, using training data from base classes with
sufficient samples. The reweighting module transforms a few support examples
from the novel classes to a global vector that indicates the importance or
relevance of meta features for detecting the corresponding objects. These two
modules, together with a detection prediction module, are trained end-to-end
based on an episodic few-shot learning scheme and a carefully designed loss
function. Through extensive experiments we demonstrate that our model
outperforms well-established baselines by a large margin for few-shot object
detection, on multiple datasets and settings. We also present analysis on
various aspects of our proposed model, aiming to provide some inspiration for
future few-shot detection works.
