  Active learning aims to reduce labeling costs by selecting only the most
informative samples on a dataset. Few existing works have addressed active
learning for object detection. Most of these methods are based on multiple
models or are straightforward extensions of classification methods, hence
estimate an image's informativeness using only the classification head. In this
paper, we propose a novel deep active learning approach for object detection.
Our approach relies on mixture density networks that estimate a probabilistic
distribution for each localization and classification head's output. We
explicitly estimate the aleatoric and epistemic uncertainty in a single forward
pass of a single model. Our method uses a scoring function that aggregates
these two types of uncertainties for both heads to obtain every image's
informativeness score. We demonstrate the efficacy of our approach in PASCAL
VOC and MS-COCO datasets. Our approach outperforms single-model based methods
and performs on par with multi-model based methods at a fraction of the
computing cost.
