Class-agnostic object counting aims to count object instances of an arbitrary
class at test time. It is challenging but also enables many potential
applications. Current methods require human-annotated exemplars as inputs which
are often unavailable for novel categories, especially for autonomous systems.
Thus, we propose zero-shot object counting (ZSC), a new setting where only the
class name is available during test time. This obviates the need for human
annotators and enables automated operation. To perform ZSC, we propose finding
a few object crops from the input image and use them as counting exemplars. The
goal is to identify patches containing the objects of interest while also being
visually representative for all instances in the image. To do this, we first
construct class prototypes using large language-vision models, including CLIP
and Stable Diffusion, to select the patches containing the target objects.
Furthermore, we propose a ranking model that estimates the counting error of
each patch to select the most suitable exemplars for counting. Experimental
results on a recent class-agnostic counting dataset, FSC-147, validate the
effectiveness of our method.

零样本目标计数 (ZSC) 是一种新的设置，只需类名作为测试时间的输入。我们提出了找到几个目标裁剪样本并将其用作计数示例的方法，该方法消除了对人工注释者的需求并实现了自动化操作。通过使用大型语言 - 视觉模型构建类原型，我们选择包含目标对象的补丁，同时使用排名模型评估每个补丁的计数误差并选择最合适的示例用于计数。在最近的类别无关计数数据集 FSC-147 上的实验结果验证了我们方法的有效性。