| סיכום: | Current class-agnostic counting methods can
generalise to unseen classes but usually require reference
images to define the type of object to be counted, as well
as instance annotations during training. Reference-less
class-agnostic counting is an emerging field that identifies
counting as, at its core, a repetition-recognition task. Such
methods facilitate counting on a changing set composition.
We show that a general feature space with global context
can enumerate instances in an image without a prior on
the object type present. Specifically, we demonstrate that
regression from vision transformer features without pointlevel supervision or reference images is superior to other
reference-less methods and is competitive with methods
that use reference images. We show this on the current
standard few-shot counting dataset FSC-147. We also
propose an improved dataset, FSC-133, which removes
errors, ambiguities, and repeated images from FSC-147
and demonstrate similar performance on it. To the best
of our knowledge, we are the first weakly-supervised
reference-less class-agnostic counting method.
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