  Low-shot counters estimate the number of objects corresponding to a selected
category, based on only few or no exemplars annotated in the image. The current
state-of-the-art estimates the total counts as the sum over the object location
density map, but does not provide individual object locations and sizes, which
are crucial for many applications. This is addressed by detection-based
counters, which, however fall behind in the total count accuracy. Furthermore,
both approaches tend to overestimate the counts in the presence of other object
classes due to many false positives. We propose DAVE, a low-shot counter based
on a detect-and-verify paradigm, that avoids the aforementioned issues by first
generating a high-recall detection set and then verifying the detections to
identify and remove the outliers. This jointly increases the recall and
precision, leading to accurate counts. DAVE outperforms the top density-based
counters by ~20% in the total count MAE, it outperforms the most recent
detection-based counter by ~20% in detection quality and sets a new
state-of-the-art in zero-shot as well as text-prompt-based counting.
