Large-scale well-annotated datasets are of great importance for training an effective object detector. However, obtaining accurate bounding box annotations is laborious and demanding. Unfortunately, the resultant noisy bounding boxes could cause corrupt supervision signals and thus diminish detection performance. Motivated by the observation that the real ground-truth is usually situated in the aggregation region of the proposals assigned to a noisy ground-truth, we propose DIStribution-aware CalibratiOn (DISCO) to model the spatial distribution of proposals for calibrating supervision signals. In DISCO, spatial distribution modeling is performed to statistically extract the potential locations of objects. Based on the modeled distribution, three distribution-aware techniques, i.e., distribution-aware proposal augmentation (DA-Aug), distribution-aware box refinement (DA-Ref), and distribution-aware confidence estimation (DA-Est), are developed to improve classification, localization, and interpretability, respectively. Extensive experiments on large-scale noisy image datasets (i.e., Pascal VOC and MS-COCO) demonstrate that DISCO can achieve state-of-the-art detection performance, especially at high noise levels.

翻译：大规模高质量标注数据集对于训练有效的目标检测器至关重要。然而，获取精确的边界框标注既费力又要求严格。不幸的是，由此产生的噪声边界框可能导致错误的监督信号，进而降低检测性能。基于真实标注通常位于分配给噪声真实标注的提议区域聚集区域的观察，我们提出面向分布校准方法（DISCO）来建模提议的空间分布以校准监督信号。在DISCO中，执行空间分布建模以统计提取对象的潜在位置。基于建模的分布，开发了三种分布感知技术，即分布感知提议增强（DA-Aug）、分布感知边界框细化（DA-Ref）和分布感知置信度估计（DA-Est），分别用于改进分类、定位和可解释性。在大型噪声图像数据集（即Pascal VOC和MS-COCO）上的大量实验表明，DISCO能够实现最先进的检测性能，尤其是在高噪声水平下。