Conventional training of a deep CNN based object detector demands a large
number of bounding box annotations, which may be unavailable for rare
categories. In this work we develop a few-shot object detector that can learn
to detect novel objects from only a few annotated examples. Our proposed model
leverages fully labeled base classes and quickly adapts to novel classes, using
a meta feature learner and a reweighting module within a one-stage detection
architecture. The feature learner extracts meta features that are generalizable
to detect novel object classes, using training data from base classes with
sufficient samples. The reweighting module transforms a few support examples
from the novel classes to a global vector that indicates the importance or
relevance of meta features for detecting the corresponding objects. These two
modules, together with a detection prediction module, are trained end-to-end
based on an episodic few-shot learning scheme and a carefully designed loss
function. Through extensive experiments we demonstrate that our model
outperforms well-established baselines by a large margin for few-shot object
detection, on multiple datasets and settings. We also present analysis on
various aspects of our proposed model, aiming to provide some inspiration for
future few-shot detection works.

本文提出了一种基于深度卷积神经网络的少样本目标检测方法，通过元特征学习与重新加权模块，快速适应新的物体分类。实验证明该模型在多个数据集和设置上均能显著优于现有少样本检测模型。