DETRs learn an optimal specialist strategy via the Hungarian loss, motivating the new Object-level Calibration Error (OCE) metric and an image-level post-hoc uncertainty quantification framework.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks ,
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Uncertainty Quantification in Detection Transformers: Object-Level Calibration and Image-Level Reliability
DETRs learn an optimal specialist strategy via the Hungarian loss, motivating the new Object-level Calibration Error (OCE) metric and an image-level post-hoc uncertainty quantification framework.