论文标题
多尺度的阳性样品细化，用于几个弹出对象检测
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
论文作者
论文摘要
很少有射击对象检测（FSOD）有助于检测器适应几乎没有培训实例的看不见的类，并且当手动注释耗时或数据采集时，很有用。与以前利用几乎没有射击分类技术来促进FSOD的尝试不同，这项工作突出了处理规模变化问题的必要性，这是由于独特的样本分布而具有挑战性的。为此，我们提出了一种多尺度的阳性样品改进（MPSR）方法，以丰富FSOD中的对象尺度。它生成多尺度的阳性样品作为对象金字塔，并在各种尺度上完善预测。我们通过将其作为辅助分支整合到更快的R-CNN的流行架构中，从而证明了它的优势，并提供了强大的FSOD解决方案。对Pascal VOC和MS Coco进行了几项实验，所提出的方法实现了最先进的结果，并明显优于其他同行，这显示了其有效性。代码可在https://github.com/jiaxi-wu/mpsr上找到。
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.
