Object detectors trained with weak annotations are affordable alternatives to fully-supervised counterparts. However, there is still a significant performance gap between them. We propose to narrow this gap by fine-tuning a base pre-trained weakly-supervised detector with a few fully-annotated samples automatically selected from the training set using ``box-in-box'' (BiB), a novel active learning strategy designed specifically to address the well-documented failure modes of weakly-supervised detectors. Experiments on the VOC07 and COCO benchmarks show that BiB outperforms other active learning techniques and significantly improves the base weakly-supervised detector's performance with only a few fully-annotated images per class. BiB reaches 97% of the performance of fully-supervised Fast RCNN with only 10% of fully-annotated images on VOC07. On COCO, using on average 10 fully-annotated images per class, or equivalently 1% of the training set, BiB also reduces the performance gap (in AP) between the weakly-supervised detector and the fully-supervised Fast RCNN by over 70%, showing a good trade-off between performance and data efficiency. Our code is publicly available at https://github.com/huyvvo/BiB.

通过使用“盒中盒”（BiB）主动学习策略，选择训练集中自动选定的少量完全注释的样本，对基础预训练的弱监督检测器进行微调，从而缩小其与完全监督检测器之间的性能差距，并显著提高其性能。在 VOC07 和 COCO 基准测试中，BiB 能够超越其他主动学习技术，仅使用每个类别的少量全注释图像即可使弱监督检测器的性能接近完全监督 Fast RCNN 的性能。代码公开可用于 https URL。

弱监督目标检测的主动学习策略