  Conventional methods for object detection usually require substantial amounts
of training data and annotated bounding boxes. If there are only a few training
data and annotations, the object detectors easily overfit and fail to
generalize. It exposes the practical weakness of the object detectors. On the
other hand, human can easily master new reasoning rules with only a few
demonstrations using previously learned knowledge. In this paper, we introduce
a few-shot object detection via knowledge transfer, which aims to detect
objects from a few training examples. Central to our method is prototypical
knowledge transfer with an attached meta-learner. The meta-learner takes
support set images that include the few examples of the novel categories and
base categories, and predicts prototypes that represent each category as a
vector. Then, the prototypes reweight each RoI (Region-of-Interest) feature
vector from a query image to remodels R-CNN predictor heads. To facilitate the
remodeling process, we predict the prototypes under a graph structure, which
propagates information of the correlated base categories to the novel
categories with explicit guidance of prior knowledge that represents
correlations among categories. Extensive experiments on the PASCAL VOC dataset
verifies the effectiveness of the proposed method.
