On Model Calibration for Long-Tailed Object Detection and Instance Segmentation
This work addresses the challenge of improving detection and segmentation accuracy for underrepresented classes in long-tailed datasets, which is crucial for real-world applications with imbalanced data, though it is incremental as it builds on existing calibration approaches.
The paper tackles the problem of model bias towards frequent objects in long-tailed object detection and instance segmentation by proposing NorCal, a post-processing calibration method that reweights predicted scores based on class sample sizes, improving performance across rare, common, and frequent classes on the LVIS dataset.
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. Our code is publicly available at https://github.com/tydpan/NorCal/.