  Expensive bounding-box annotations have limited the development of object
detection task. Thus, it is necessary to focus on more challenging task of
few-shot object detection. It requires the detector to recognize objects of
novel classes with only a few training samples. Nowadays, many existing popular
methods adopting training way similar to meta-learning have achieved promising
performance, such as Meta R-CNN series. However, support data is only used as
the class attention to guide the detecting of query images each time. Their
relevance to each other remains unexploited. Moreover, a lot of recent works
treat the support data and query images as independent branch without
considering the relationship between them. To address this issue, we propose a
dynamic relevance learning model, which utilizes the relationship between all
support images and Region of Interest (RoI) on the query images to construct a
dynamic graph convolutional network (GCN). By adjusting the prediction
distribution of the base detector using the output of this GCN, the proposed
model serves as a hard auxiliary classification task, which guides the detector
to improve the class representation implicitly. Comprehensive experiments have
been conducted on Pascal VOC and MS-COCO dataset. The proposed model achieves
the best overall performance, which shows its effectiveness of learning more
generalized features. Our code is available at
https://github.com/liuweijie19980216/DRL-for-FSOD.
