  In recent years, there are many applications of object detection in remote
sensing field, which demands a great number of labeled data. However, in many
cases, data is extremely rare. In this paper, we proposed a few-shot object
detector which is designed for detecting novel objects based on only a few
examples. Through fully leveraging labeled base classes, our model that is
composed of a feature-extractor, a feature attention highlight module as well
as a two-stage detection backend can quickly adapt to novel classes. The
pre-trained feature extractor whose parameters are shared produces general
features. While the feature attention highlight module is designed to be
light-weighted and simple in order to fit the few-shot cases. Although it is
simple, the information provided by it in a serial way is helpful to make the
general features to be specific for few-shot objects. Then the object-specific
features are delivered to the two-stage detection backend for the detection
results. The experiments demonstrate the effectiveness of the proposed method
for few-shot cases.
