  Few-shot object detection (FSOD) aims to detect never-seen objects using few
examples. This field sees recent improvement owing to the meta-learning
techniques by learning how to match between the query image and few-shot class
examples, such that the learned model can generalize to few-shot novel classes.
However, currently, most of the meta-learning-based methods perform pairwise
matching between query image regions (usually proposals) and novel classes
separately, therefore failing to take into account multiple relationships among
them. In this paper, we propose a novel FSOD model using heterogeneous graph
convolutional networks. Through efficient message passing among all the
proposal and class nodes with three different types of edges, we could obtain
context-aware proposal features and query-adaptive, multiclass-enhanced
prototype representations for each class, which could help promote the pairwise
matching and improve final FSOD accuracy. Extensive experimental results show
that our proposed model, denoted as QA-FewDet, outperforms the current
state-of-the-art approaches on the PASCAL VOC and MSCOCO FSOD benchmarks under
different shots and evaluation metrics.
