  The great success that deep models have achieved in the past is mainly owed
to large amounts of labeled training data. However, the acquisition of labeled
data for new tasks aside from existing benchmarks is both challenging and
costly. Active learning can make the process of labeling new data more
efficient by selecting unlabeled samples which, when labeled, are expected to
improve the model the most. In this paper, we combine a novel method of active
learning for object detection with an incremental learning scheme to enable
continuous exploration of new unlabeled datasets. We propose a set of
uncertainty-based active learning metrics suitable for most object detectors.
Furthermore, we present an approach to leverage class imbalances during sample
selection. All methods are evaluated systematically in a continuous exploration
context on the PASCAL VOC 2012 dataset.
