Zero-Shot Object Counting with Language-Vision Models
This enables automated object counting for novel categories, benefiting autonomous systems by eliminating the need for human annotators, though it is incremental as it builds on existing class-agnostic counting methods.
The paper tackles the problem of counting objects of arbitrary classes without human-annotated exemplars by proposing a zero-shot object counting setting that uses only class names, achieving validated effectiveness on the FSC-147 dataset.
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.