Deep Active Learning for Object Detection with Mixture Density Networks

Active learning aims to reduce the labeling costs by selecting only samples that are informative to improve the accuracy of the network. Few existing works have addressed the problem of active learning for object detection, and most of them estimate the informativeness of an image based only on the classification head, neglecting the influence of the localization head. In this paper, we propose a novel deep active learning approach for object detection. Our approach relies on mixture density networks to provide a mixture distribution for every output parameter. Based on these distributions, our approach is able to compute, separately and in a single forward pass of a single model, the epistemic and aleatoric uncertainty. We further propose a more efficient approach to reduce the computational cost of the mixture model. For active learning, we propose a scoring function that aggregates uncertainties from both the classification and the localization outputs of the network. Our extensive set of experiments on PASCAL VOC and COCO demonstrates that our modification to the object detection network yields better accuracy compared to the original one and, for active learning, our approach outperforms single-model based methods and performs on par when compared to methods using multiple models while requiring significantly lower computational cost. In addition, we show that our approach scales to different object detection networks and datasets acquired actively using our approach to transfer to different networks.