论文标题
通过渐进知识转移来增强弱监督的对象检测
Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer
论文作者
论文摘要
在本文中，我们提出了一个有效的知识转移框架，以借助外部全面注销的源数据集提高弱监督的对象检测精度，该数据集可能与目标域重叠。由于存在许多现成的检测数据集，因此此设置具有巨大的实际价值。要更有效地利用源数据集，我们建议通过一级通用检测器从源域转移知识，并学习目标域检测器。目标域检测器在每个迭代中挖掘出的盒子级伪基地面真相有效地改善了一级通用检测器。因此，源数据集中的知识更加彻底地利用和利用。广泛的实验是用Pascal VOC 2007作为目标弱通知的数据集和可可/成像网作为源完全注销的数据集进行的。借助拟议的解决方案，我们在VOC测试集中获得了$ 59.7 \％$检测性能的地图，并在带有开采的Pseudo Ground Truth的速度更快的RCNN后，地图为60.2 \％$。这比相关文献中的任何以前已知的结果要好得多，并在知识转移设置下设置了弱监督对象检测的新最新。代码：\ url {https://github.com/mikuhatsune/wsod_transfer}。
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}.
