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Found 2 papers, 2 papers with code
Scaling Novel Object Detection with Weakly Supervised Detection Transformers
A critical object detection task is finetuning an existing model to detect novel objects, but the standard workflow requires bounding box annotations which are time-consuming and expensive to collect.
We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric Uncertainty
The geometric uncertainty maps generated by our BCNN capture distributions of sigmoid values that are interpretable as confidence intervals, critical for applications that rely on deep learning for high-consequence decisions.
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