Active Learning for Deep Object Detection via Probabilistic Modeling
This work addresses the challenge of high labeling costs for object detection tasks, offering a more efficient solution compared to existing methods.
The paper tackles the problem of reducing labeling costs in object detection by proposing a novel active learning method that uses probabilistic modeling to estimate aleatoric and epistemic uncertainties in a single forward pass, achieving performance on par with multi-model methods at a fraction of the computing cost on PASCAL VOC and MS-COCO datasets.
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.