  While recent supervised methods for reference-based object counting continue
to improve the performance on benchmark datasets, they have to rely on small
datasets due to the cost associated with manually annotating dozens of objects
in images. We propose UnCounTR, a model that can learn this task without
requiring any manual annotations. To this end, we construct "Self-Collages",
images with various pasted objects as training samples, that provide a rich
learning signal covering arbitrary object types and counts. Our method builds
on existing unsupervised representations and segmentation techniques to
successfully demonstrate for the first time the ability of reference-based
counting without manual supervision. Our experiments show that our method not
only outperforms simple baselines and generic models such as FasterRCNN and
DETR, but also matches the performance of supervised counting models in some
domains.
