  Few-shot object detection (FSOD) aims to detect objects using only few
examples. It's critically needed for many practical applications but so far
remains challenging. We propose a meta-learning based few-shot object detection
method by transferring meta-knowledge learned from data-abundant base classes
to data-scarce novel classes. Our method incorporates a coarse-to-fine approach
into the proposal based object detection framework and integrates prototype
based classifiers into both the proposal generation and classification stages.
To improve proposal generation for few-shot novel classes, we propose to learn
a lightweight matching network to measure the similarity between each spatial
position in the query image feature map and spatially-pooled class features,
instead of the traditional object/nonobject classifier, thus generating
category-specific proposals and improving proposal recall for novel classes. To
address the spatial misalignment between generated proposals and few-shot class
examples, we propose a novel attentive feature alignment method, thus improving
the performance of few-shot object detection. Meanwhile we jointly learn a
Faster R-CNN detection head for base classes. Extensive experiments conducted
on multiple FSOD benchmarks show our proposed approach achieves state of the
art results under (incremental) few-shot learning settings.
