Vanilla models for object detection and instance segmentation suffer from the heavy bias toward detecting frequent objects in the long-tailed setting. Existing methods address this issue mostly during training, e.g., by re-sampling or re-weighting. In this paper, we investigate a largely overlooked approach -- post-processing calibration of confidence scores. We propose NorCal, Normalized Calibration for long-tailed object detection and instance segmentation, a simple and straightforward recipe that reweighs the predicted scores of each class by its training sample size. We show that separately handling the background class and normalizing the scores over classes for each proposal are keys to achieving superior performance. On the LVIS dataset, NorCal can effectively improve nearly all the baseline models not only on rare classes but also on common and frequent classes. Finally, we conduct extensive analysis and ablation studies to offer insights into various modeling choices and mechanisms of our approach.

本研究探讨了一种后处理校准置信度分数的方法，提出了NorCal，基于训练样本大小来重新加权每个类别的预测分数，通过将背景类别和每个候选区域上类别的分数进行归一化来在长尾场景下增强性能，这种方法可以显著提高几乎所有基线模型的表现。

关于长尾物体检测和实例分割的模型校准