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 query 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 needing human in-the-loop annotations. We also show that this performance transfers to FSC-147, the standard class-agnostic counting dataset. MCAC is available at MCAC.active.vision and ABC123 is available at ABC123.active.vision.

翻译：类别无关计数方法可以枚举任意类别的对象，在许多领域提供了巨大的实用性。先前的工作实用性有限，因为它们要么需要一组待计数类别的示例，要么要求查询图像仅包含单一类型的对象。这些缺陷的一个重要因素是缺乏一个合适的数据集来处理存在多种对象场景下的计数问题。为了解决这些问题，我们提出了首个多类别类别无关计数数据集（MCAC）以及一种盲计数器（ABC123），该方法能够在训练或推理期间不使用类别示例的情况下，同时计数多种类型的对象。ABC123引入了一种新的范式，即不再需要示例来指导枚举过程，而是在计数阶段之后找到示例，以帮助用户理解生成的输出。我们证明，ABC123在MCAC上优于现有方法，且无需人工在环标注。我们还表明，这种性能可以迁移到标准类别无关计数数据集FSC-147。MCAC可在MCAC.active.vision获取，ABC123可在ABC123.active.vision获取。