A Method for Low-shot Object Counting and DetectionPelhan, Jer (Avtor) Kristan, Matej (Mentor) computer visionobject countingobject detectionfew-shot learningWe tackle the problem of few-shot object counting and detection of arbitrary object categories using only a small number of annotated instances, namely exemplars. The task of the method is to count and detect all objects that are part of the same semantic category as the exemplars but may vary widely in visual appearance. Methods currently address this problem by generalizing the appearance of the exemplar objects, allowing them to count effectively. The generalization capacity leads to high recall, but also to low precision, due to non-discriminative counting. Few-shot counting methods predict solely the total count of objects and do not provide estimations of their locations, which is crucial with many applications. We propose a novel method DAVE (Detect and Verify), which aims to bridge the gap between traditional few-shot counting methods and the emerging field of few-shot counting and detection, by predicting accurate count and locations of objects. We introduce a detect-and-verify paradigm into few-shot counting, achieving both high recall and precision rates. DAVE outperforms the most recent detection counter by 20% in terms of AP50 and decreases the counting error of the top-performing counter by 20% in terms of MAE. DAVE further outperforms all 0-shot counters in terms of RMSE, and achieves on-par AP50 performance as the best few-shot counting and detection method.20232023-09-12 15:05:00Magistrsko delo/naloga149978sl