I just stumbled upon a new metric I've never heard about called False Positive rate at K recall (FPR-K).
Searching the internet I just managed to find more papers using the metric but none of them actually explain how is it calculated.
The paper was on matching feature points in multi-modal images.
I know what is AP@K, used in tasks involving some thresholding (i.e. image detection where we're using intersection over union to calculate our TP/FP etc).
My assumption regarding FPR-95 is that there's some thresholding involved as well (else why the "at K" term?), and as with thresholds, they control the precision / recall rate. So we set a threshold causing 95% recall, and now we calculate the FP rate (FP / FP + TN).
Can anyone validate or invalidate my assumption?