A Component-based Systematic Review of Pool-based Active Learning for 2D Object Detection

Abstract

Active learning can help reduce the labeling burden in training deep object detection models by strategically selecting the most informative data points for labeling from unlabeled data. Different approaches to this task have been proposed in recent years, with differences often found in their approaches to the specific components. Consequently, this study breaks down active learning methods into their individual components and examines the diverse strategies associated with each intending to shed light on areas that have not yet been explored in depth. Doing so, the study offers valuable insights into potential research directions in this area.