Annotating such datasets is highly time consuming and expensive, which motivates the development of weakly supervised and few-shot object detection methods. However, these methods largely underperform with respect to their strongly supervised counterpart, as weak training signals \emph{often} result in partial or oversized detections. Towards solving this problem we introduce, for the first time, an online annotation module (OAM) that learns to generate a many-shot set of \emph{reliable} annotations from a larger volume of weakly labelled images. Our OAM can be jointly trained with any fully supervised two-stage object detection method, providing additional training annotations on the fly. This results in a fully end-to-end strategy that only requires a low-shot set of fully annotated images. The integration of the OAM with Fast(er) R-CNN improves their performance by $17\%$ mAP, $9\%$ AP50 on PASCAL VOC 2007 and MS-COCO benchmarks, and significantly outperforms competing methods using mixed supervision.

该论文提出了一种使用在线标注模块（OAM）从弱标注图像生成一组可靠的注释，可与任何完全监督的两阶段对象检测方法联合训练，从而实现了仅需要一组低标注图像的完全端到端策略。将OAM与Fast(er) R-CNN相结合，可在PASCAL VOC 2007和MS-COCO基准测试上将其性能提高了17％ mAP，9％ AP50，并且明显优于使用混合监督的竞争方法。

从少样本到多样本：使用混合监督学习注释目标检测