  Conventional methods for object detection typically require a substantial
amount of training data and preparing such high-quality training data is very
labor-intensive. In this paper, we propose a novel few-shot object detection
network that aims at detecting objects of unseen categories with only a few
annotated examples. Central to our method are our Attention-RPN, Multi-Relation
Detector and Contrastive Training strategy, which exploit the similarity
between the few shot support set and query set to detect novel objects while
suppressing false detection in the background. To train our network, we
contribute a new dataset that contains 1000 categories of various objects with
high-quality annotations. To the best of our knowledge, this is one of the
first datasets specifically designed for few-shot object detection. Once our
few-shot network is trained, it can detect objects of unseen categories without
further training or fine-tuning. Our method is general and has a wide range of
potential applications. We produce a new state-of-the-art performance on
different datasets in the few-shot setting. The dataset link is
https://github.com/fanq15/Few-Shot-Object-Detection-Dataset.
