  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 as open-world
threshold calibration. Existing posthoc threshold calibration methods, reliant
on inductive inference and requiring a calibration dataset with a similar
distance distribution as the test data, often prove ineffective in open-world
scenarios. To address this, we introduce OpenGCN, a Graph Neural Network-based
transductive threshold calibration method with enhanced adaptability and
robustness. OpenGCN learns to predict pairwise connectivity for the unlabeled
test instances embedded in a graph to determine its TPR and TNR at various
distance thresholds, allowing for transductive inference of the distance
thresholds which also incorporates test-time information. Extensive experiments
across open-world visual recognition benchmarks validate OpenGCN's superiority
over existing posthoc calibration methods for open-world threshold calibration.
