The objective of this paper is few-shot object detection (FSOD) -- the task
of expanding an object detector for a new category given only a few instances
for training. We introduce a simple pseudo-labelling method to source
high-quality pseudo-annotations from the training set, for each new category,
vastly increasing the number of training instances and reducing class
imbalance; our method finds previously unlabelled instances. Na\"ively training
with model predictions yields sub-optimal performance; we present two novel
methods to improve the precision of the pseudo-labelling process: first, we
introduce a verification technique to remove candidate detections with
incorrect class labels; second, we train a specialised model to correct poor
quality bounding boxes. After these two novel steps, we obtain a large set of
high-quality pseudo-annotations that allow our final detector to be trained
end-to-end. Additionally, we demonstrate our method maintains base class
performance, and the utility of simple augmentations in FSOD. While
benchmarking on PASCAL VOC and MS-COCO, our method achieves state-of-the-art or
second-best performance compared to existing approaches across all number of
shots.

该论文介绍了一种用于少量数据情况下目标检测的伪标注方法，可以从训练数据中找到高质量伪标注，显著增加训练实例数量，降低类别不平衡问题，通过验证技术和训练一个专门的模型来纠正盒子边框的质量，其中展示了算法在 PASCAL VOC 和 MS-COCO 数据集上的表现，与现有方法相比获得了最优状态或次优状态。