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

TL;DR

This paper introduces NorCal, a simple post-processing calibration method that reweighs class scores based on training sample sizes, significantly improving long-tailed object detection and segmentation performance across all class frequencies.

Contribution

It proposes a novel post-processing calibration technique, NorCal, that effectively enhances long-tailed detection and segmentation by score normalization and class-specific reweighting.

Findings

Improves performance on rare, common, and frequent classes.

Effective across multiple baseline models on LVIS dataset.

Provides insights through extensive analysis and ablation studies.

Abstract

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/.