Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited. Unlike previous attempts that exploit few-shot classification techniques to facilitate FSOD, this work highlights the necessity of handling the problem of scale variations, which is challenging due to the unique sample distribution. To this end, we propose a Multi-scale Positive Sample Refinement (MPSR) approach to enrich object scales in FSOD. It generates multi-scale positive samples as object pyramids and refines the prediction at various scales. We demonstrate its advantage by integrating it as an auxiliary branch to the popular architecture of Faster R-CNN with FPN, delivering a strong FSOD solution. Several experiments are conducted on PASCAL VOC and MS COCO, and the proposed approach achieves state of the art results and significantly outperforms other counterparts, which shows its effectiveness. Code is available at https://github.com/jiaxi-wu/MPSR.

提出了一种多尺度正样本精炼（MPSR）方法，通过生成目标金字塔来丰富FSOD的对象尺度，并在不同的尺度上优化预测，将其作为辅助分支集成到 Faster R-CNN with FPN 的流行架构中，该方法在PASCAL VOC和MS COCO上取得了最先进的结果和显著的超越了其他对手。

多尺度正样本精炼在少样本物体检测中的应用