On Model Calibration for Long-Tailed Object Detection and Instance Segmentation

Understand

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.