ABC Easy as 123: A Blind Counter for Exemplar-Free Multi-Class
  Class-agnostic Counting
Michael A. Hobley Victor A. Prisacariu
Class-agnostic counting methods enumerate objects of an arbitrary class,
providing tremendous utility in many fields. Prior works have limited
usefulness as they require either a set of examples of the type to be counted
or that the image contains only a single type of object. A significant factor
in these shortcomings is the lack of a dataset to properly address counting in
settings with more than one kind of object present. To address these issues, we
propose the first Multi-class, Class-Agnostic Counting dataset (MCAC) and A
Blind Counter (ABC123), a method that can count multiple types of objects
simultaneously without using examples of type during training or inference.
ABC123 introduces a new paradigm where instead of requiring exemplars to guide
the enumeration, examples are found after the counting stage to help a user
understand the generated outputs. We show that ABC123 outperforms contemporary
methods on MCAC without the requirement of human in-the-loop annotations. We
also show that this performance transfers to FSC-147, the standard
class-agnostic counting dataset.