  Few-shot learning has recently emerged as a new challenge in the deep
learning field: unlike conventional methods that train the deep neural networks
(DNNs) with a large number of labeled data, it asks for the generalization of
DNNs on new classes with few annotated samples. Recent advances in few-shot
learning mainly focus on image classification while in this paper we focus on
object detection. The initial explorations in few-shot object detection tend to
simulate a classification scenario by using the positive proposals in images
with respect to certain object class while discarding the negative proposals of
that class. Negatives, especially hard negatives, however, are essential to the
embedding space learning in few-shot object detection. In this paper, we
restore the negative information in few-shot object detection by introducing a
new negative- and positive-representative based metric learning framework and a
new inference scheme with negative and positive representatives. We build our
work on a recent few-shot pipeline RepMet with several new modules to encode
negative information for both training and testing. Extensive experiments on
ImageNet-LOC and PASCAL VOC show our method substantially improves the
state-of-the-art few-shot object detection solutions. Our code is available at
https://github.com/yang-yk/NP-RepMet.
