Label, verify, correct: a simple few shot object detection method - Kaul, P; Xie, W; Zisserman, A (IEEE, 2022)
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 highquality 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¨ıvely training with model predictions yields suboptimal 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 highquality 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 stateof-the-art or second-best performance compared to existing approaches across all number of shots.