Zero-Shot Object Counting with Language-Vision Models
Jingyi Xu Hieu Le Dimitris Samaras
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.