Learning for Transductive Threshold Calibration in Open-World Recognition

arXiv:2305.12039v2 Announce Type: replace
Abstract: In deep metric learning for visual recognition, the calibration of distance thresholds is crucial for achieving desired model performance in the true positive rates (TPR) or true negative rates (TNR). However, calibrating this threshold presents challenges in open-world scenarios, where the test classes can be entirely disjoint from those encountered during training. We define the problem of finding distance thresholds for a trained embedding model to achieve target performance metrics over unseen open-world test classes …

abstract arxiv challenges cs.cv however negative open-world performance positive recognition test threshold true type visual world