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。

标记、验证、纠正: 一种简单的少样本目标检测方法