Active learning aims to improve the performance of task model by selecting
the most informative samples with a limited budget. Unlike most recent works
that focused on applying active learning for image classification, we propose
an effective Consistency-based Active Learning method for object Detection
(CALD), which fully explores the consistency between original and augmented
data. CALD has three appealing benefits. (i) CALD is systematically designed by
investigating the weaknesses of existing active learning methods, which do not
take the unique challenges of object detection into account. (ii) CALD unifies
box regression and classification with a single metric, which is not concerned
by active learning methods for classification. CALD also focuses on the most
informative local region rather than the whole image, which is beneficial for
object detection. (iii) CALD not only gauges individual information for sample
selection, but also leverages mutual information to encourage a balanced data
distribution. Extensive experiments show that CALD significantly outperforms
existing state-of-the-art task-agnostic and detection-specific active learning
methods on general object detection datasets. Based on the Faster R-CNN
detector, CALD consistently surpasses the baseline method (random selection) by
2.9/2.8/0.8 mAP on average on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO.
Code is available at https://github.com/we1pingyu/CALD

提出了一种基于一致性的主动学习方法，即 “CALD”，该方法与现有的分类型主动学习方法不同，因其将目光投向了面向目标检测任务下的个性化挑战，并旨在通过在原始数据和增强数据之间探索一致性来提高任务模型的性能。CALD 方法在目标检测领域取得了优异的表现，特别是在 PASCAL VOC 2007、PASCAL VOC 2012 和 MS COCO 数据集上，相比以前的方法，平均 mAP 分别提高了 2.9 / 2.8 / 0.8。