Learning to detect novel objects from few annotated examples is of great
practical importance. A particularly challenging yet common regime occurs when
there are extremely limited examples (less than three). One critical factor in
improving few-shot detection is to address the lack of variation in training
data. We propose to build a better model of variation for novel classes by
transferring the shared within-class variation from base classes. To this end,
we introduce a hallucinator network that learns to generate additional, useful
training examples in the region of interest (RoI) feature space, and
incorporate it into a modern object detection model. Our approach yields
significant performance improvements on two state-of-the-art few-shot detectors
with different proposal generation procedures. In particular, we achieve new
state of the art in the extremely-few-shot regime on the challenging COCO
benchmark.

使用幻像网络在 RoI 特征空间中生成特定类别的附加训练样本，并将其整合入现代目标检测模型中，以改善少样本检测。该方法在 COCO 基准测试中实现了新的极少样本检测率的表现，并在两个最先进的少样本检测器上取得了显着的性能改进。