Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. However, detecting novel categories with only a few samples usually leads to the problem of misclassification. In FSOD, we notice the false positive (FP) of novel categories is prominent, in which the base categories are often recognized as novel ones. To address this issue, a novel data augmentation pipeline that Crops the Novel instances and Pastes them on the selected Base images, called CNPB, is proposed. There are two key questions to be answered: (1) How to select useful base images? and (2) How to combine novel and base data? We design a multi-step selection strategy to find useful base data. Specifically, we first discover the base images which contain the FP of novel categories and select a certain amount of samples from them for the base and novel categories balance. Then the bad cases, such as the base images that have unlabeled ground truth or easily confused base instances, are removed by using CLIP. Finally, the same category strategy is adopted, in which a novel instance with category n is pasted on the base image with the FP of n. During combination, a novel instance is cropped and randomly down-sized, and thus pasted at the assigned optimal location from the randomly generated candidates in a selected base image. Our method is simple yet effective and can be easy to plug into existing FSOD methods, demonstrating significant potential for use. Extensive experiments on PASCAL VOC and MS COCO validate the effectiveness of our method.

翻译：小样本目标检测（FSOD）旨在仅利用少量训练实例将目标检测器扩展到新类别。然而，仅通过少量样本检测新类别通常会导致误分类问题。在FSOD中，我们注意到新类别的假正例（FP）尤为突出，基类常被误识别为新类别。为解决该问题，提出一种名为CNPB的新型数据增强流水线——裁剪新实例并粘贴到选定的基类图像上。需回答两个关键问题：（1）如何选择有效的基类图像？（2）如何融合新类与基类数据？我们设计了一种多步筛选策略以获取有效基类数据。具体而言，首先发现包含新类别假正例的基类图像，并从其中选取一定数量样本以实现基类与新类别的平衡。随后利用CLIP去除不良样本，例如存在未标注真值或易混淆基类实例的图像。最后采用同类别策略，将类别为n的新实例粘贴到包含n类假正例的基类图像上。在融合过程中，裁剪新实例并随机缩小尺寸，将其粘贴至选定基类图像中随机生成候选位置的最优分配点。该方法简洁高效，易于嵌入现有FSOD方法，展现出显著的应用潜力。在PASCAL VOC和MS COCO上的大量实验验证了本方法的有效性。