  While fine-tuning based methods for few-shot object detection have achieved
remarkable progress, a crucial challenge that has not been addressed well is
the potential class-specific overfitting on base classes and sample-specific
overfitting on novel classes. In this work we design a novel knowledge
distillation framework to guide the learning of the object detector and thereby
restrain the overfitting in both the pre-training stage on base classes and
fine-tuning stage on novel classes. To be specific, we first present a novel
Position-Aware Bag-of-Visual-Words model for learning a representative bag of
visual words (BoVW) from a limited size of image set, which is used to encode
general images based on the similarities between the learned visual words and
an image. Then we perform knowledge distillation based on the fact that an
image should have consistent BoVW representations in two different feature
spaces. To this end, we pre-learn a feature space independently from the object
detection, and encode images using BoVW in this space. The obtained BoVW
representation for an image can be considered as distilled knowledge to guide
the learning of object detector: the extracted features by the object detector
for the same image are expected to derive the consistent BoVW representations
with the distilled knowledge. Extensive experiments validate the effectiveness
of our method and demonstrate the superiority over other state-of-the-art
methods.
