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.

本文提出了一种基于混合密度网络的深度主动学习方法，该方法通过对定位和分类器的输出估计概率分布并明确估计模型的不确定性，使用一种特殊得分函数将这两种不确定性聚合在一起以获取每个图像的信息量分数，通过 PASCAL VOC 和 MS-COCO 数据集的实验证明了该方法在物体检测方面的有效性，性能优于单模型和多模型方法，而计算成本仅为之前方法的一小部分。