  We tackle the task of Class Agnostic Counting, which aims to count objects in
a novel object category at test time without any access to labeled training
data for that category. All previous class agnostic counting methods cannot
work in a fully automated setting, and require computationally expensive test
time adaptation. To address these challenges, we propose a visual counter which
operates in a fully automated setting and does not require any test time
adaptation. Our proposed approach first identifies exemplars from repeating
objects in an image, and then counts the repeating objects. We propose a novel
region proposal network for identifying the exemplars. After identifying the
exemplars, we obtain the corresponding count by using a density estimation
based Visual Counter. We evaluate our proposed approach on FSC-147 dataset, and
show that it achieves superior performance compared to the existing approaches.
