  Deep neural networks have reached high accuracy on object detection but their
success hinges on large amounts of labeled data. To reduce the labels
dependency, various active learning strategies have been proposed, typically
based on the confidence of the detector. However, these methods are biased
towards high-performing classes and can lead to acquired datasets that are not
good representatives of the testing set data. In this work, we propose a
unified framework for active learning, that considers both the uncertainty and
the robustness of the detector, ensuring that the network performs well in all
classes. Furthermore, our method leverages auto-labeling to suppress a
potential distribution drift while boosting the performance of the model.
Experiments on PASCAL VOC07+12 and MS-COCO show that our method consistently
outperforms a wide range of active learning methods, yielding up to a 7.7%
improvement in mAP, or up to 82% reduction in labeling cost. Code will be
released upon acceptance of the paper.
