D.9 Parameter Size and Inference Speed . . . . . . . . . . . . . . . . . . . . . . . .

Unfortunately, very little to no attention is paid towards addressing calibration ofDNN-based visual object detectors, that occupysimilar space and importance inmanydecision making systems astheir visual classification counterparts. In this work, we study the calibration of DNN-based object detection models, particularly under domain shift.

Large transformer-based foundation models have been commonly used as pre-trained models that can be adapted to different challenging datasets and settings with state-of-the-art generalization performance.

Previous deep learning approaches for survival analysis have primarily relied on ranking losses to improve discrimination performance, which often comes at the expense of calibration performance. To address such an issue, we propose a novel contrastive learning approach specifically designed to enhance discrimination without sacrificing calibration.

In this work, we conduct asystematic study on the calibration qualities ofGNN node predictions.