  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.
