Obtaining large-scale labeled object detection dataset can be costly and time-consuming, as it involves annotating images with bounding boxes and class labels. Thus, some specialized active learning methods have been proposed to reduce the cost by selecting either coarse-grained samples or fine-grained instances from unlabeled data for labeling. However, the former approaches suffer from redundant labeling, while the latter methods generally lead to training instability and sampling bias. To address these challenges, we propose a novel approach called Multi-scale Region-based Active Learning (MuRAL) for object detection. MuRAL identifies informative regions of various scales to reduce annotation costs for well-learned objects and improve training performance. The informative region score is designed to consider both the predicted confidence of instances and the distribution of each object category, enabling our method to focus more on difficult-to-detect classes. Moreover, MuRAL employs a scale-aware selection strategy that ensures diverse regions are selected from different scales for labeling and downstream finetuning, which enhances training stability. Our proposed method surpasses all existing coarse-grained and fine-grained baselines on Cityscapes and MS COCO datasets, and demonstrates significant improvement in difficult category performance.

翻译：获取大规模标注的目标检测数据集成本高昂且耗时，因为它需要为图像标注边界框和类别标签。为此，已有一些专门的主动学习方法通过从未标注数据中选取粗粒度样本或细粒度实例进行标注来降低成本。然而，前者方法存在冗余标注问题，后者方法则通常导致训练不稳定和采样偏差。为解决这些挑战，我们提出了一种名为多尺度区域主动学习（MuRAL）的新型目标检测方法。MuRAL能够识别不同尺度的信息区域，以降低对已学得良好目标的标注成本并提升训练性能。信息区域评分综合考虑了实例的预测置信度和各类别分布，使我们的方法更聚焦于难以检测的类别。此外，MuRAL采用了尺度感知的选择策略，确保从不同尺度中选取多样化的区域用于标注和下游微调，从而增强训练稳定性。我们提出的方法在Cityscapes和MS COCO数据集上超越了所有现有的粗粒度和细粒度基线方法，并在困难类别的性能上表现出显著提升。