  Few-shot object detection (FSOD) aims to detect objects using only a few
examples. How to adapt state-of-the-art object detectors to the few-shot domain
remains challenging. Object proposal is a key ingredient in modern object
detectors. However, the quality of proposals generated for few-shot classes
using existing methods is far worse than that of many-shot classes, e.g.,
missing boxes for few-shot classes due to misclassification or inaccurate
spatial locations with respect to true objects. To address the noisy proposal
problem, we propose a novel meta-learning based FSOD model by jointly
optimizing the few-shot proposal generation and fine-grained few-shot proposal
classification. To improve proposal generation for few-shot classes, we propose
to learn a lightweight metric-learning based prototype matching network,
instead of the conventional simple linear object/nonobject classifier, e.g.,
used in RPN. Our non-linear classifier with the feature fusion network could
improve the discriminative prototype matching and the proposal recall for
few-shot classes. To improve the fine-grained few-shot proposal classification,
we propose a novel attentive feature alignment method to address the spatial
misalignment between the noisy proposals and few-shot classes, thus improving
the performance of few-shot object detection. Meanwhile we learn a separate
Faster R-CNN detection head for many-shot base classes and show strong
performance of maintaining base-classes knowledge. Our model achieves
state-of-the-art performance on multiple FSOD benchmarks over most of the shots
and metrics.
