Instance-Aware Uncertainty for Active Learning in Object Detection

Zhipeng Zhang, Wenting Ma, Xiaohang Yuan, Yuan Hao, Meng Guo, Hongyi Tang, Zhiheng Zhou, Zhenjie Yao

Abstract: Active learning (AL) can significantly reduce the overall amount of training data required to achieve near-optimal performance. Despite the notable advancements in active learning for image recognition, there remains a lack of a lightweight active learning method specifically designed for object detection. In this paper, we embark on an investigation into the uncertainty of predicted instances’ boxes within the object detection process, addressing three distinct types of uncertainty related to position, size, and category in object detection. We define proposals after regression as instances and feature instances belong to one object as instances bags. We estimate image uncertainty by calculating the position, size, and category variances of these instance bags. In object detection tasks, experiments validate that the proposed method significantly reduces computational complexity and outperforms state-of-the-art methods by a substantial margin.