  Albeit achieving high predictive accuracy across many challenging computer
vision problems, recent studies suggest that deep neural networks (DNNs) tend
to make overconfident predictions, rendering them poorly calibrated. Most of
the existing attempts for improving DNN calibration are limited to
classification tasks and restricted to calibrating in-domain predictions.
Surprisingly, very little to no attempts have been made in studying the
calibration of object detection methods, which occupy a pivotal space in
vision-based security-sensitive, and safety-critical applications. In this
paper, we propose a new train-time technique for calibrating modern object
detection methods. It is capable of jointly calibrating multiclass confidence
and box localization by leveraging their predictive uncertainties. We perform
extensive experiments on several in-domain and out-of-domain detection
benchmarks. Results demonstrate that our proposed train-time calibration method
consistently outperforms several baselines in reducing calibration error for
both in-domain and out-of-domain predictions. Our code and models are available
at https://github.com/bimsarapathiraja/MCCL.
