  The goal of this paper is to improve the generality and accuracy of
open-vocabulary object counting in images. To improve the generality, we
repurpose an open-vocabulary detection foundation model (GroundingDINO) for the
counting task, and also extend its capabilities by introducing modules to
enable specifying the target object to count by visual exemplars. In turn,
these new capabilities - being able to specify the target object by
multi-modalites (text and exemplars) - lead to an improvement in counting
accuracy.
  We make three contributions: First, we introduce the first open-world
counting model, CountGD, where the prompt can be specified by a text
description or visual exemplars or both; Second, we show that the performance
of the model significantly improves the state of the art on multiple counting
benchmarks - when using text only, CountGD is comparable to or outperforms all
previous text-only works, and when using both text and visual exemplars, we
outperform all previous models; Third, we carry out a preliminary study into
different interactions between the text and visual exemplar prompts, including
the cases where they reinforce each other and where one restricts the other.
The code and an app to test the model are available at
https://www.robots.ox.ac.uk/~vgg/research/countgd/.
