  In this paper, we propose an effective knowledge transfer framework to boost
the weakly supervised object detection accuracy with the help of an external
fully-annotated source dataset, whose categories may not overlap with the
target domain. This setting is of great practical value due to the existence of
many off-the-shelf detection datasets. To more effectively utilize the source
dataset, we propose to iteratively transfer the knowledge from the source
domain by a one-class universal detector and learn the target-domain detector.
The box-level pseudo ground truths mined by the target-domain detector in each
iteration effectively improve the one-class universal detector. Therefore, the
knowledge in the source dataset is more thoroughly exploited and leveraged.
Extensive experiments are conducted with Pascal VOC 2007 as the target
weakly-annotated dataset and COCO/ImageNet as the source fully-annotated
dataset. With the proposed solution, we achieved an mAP of $59.7\%$ detection
performance on the VOC test set and an mAP of $60.2\%$ after retraining a fully
supervised Faster RCNN with the mined pseudo ground truths. This is
significantly better than any previously known results in related literature
and sets a new state-of-the-art of weakly supervised object detection under the
knowledge transfer setting. Code:
\url{https://github.com/mikuhatsune/wsod_transfer}.
