Zero-shot Object Counting with Good Exemplars

TL;DR

This paper introduces VA-Count, a novel framework for zero-shot object counting that enhances exemplar quality and reduces errors using vision-language models and contrastive learning, achieving superior results on multiple datasets.

Contribution

The paper proposes VA-Count, a new zero-shot counting framework with modules for exemplar enhancement and noise suppression, improving scalability and accuracy without manual annotations.

Findings

Outperforms existing methods on two datasets

Effectively identifies high-quality exemplars

Reduces negative impact of incorrect exemplars

Abstract

Zero-shot object counting (ZOC) aims to enumerate objects in images using only the names of object classes during testing, without the need for manual annotations. However, a critical challenge in current ZOC methods lies in their inability to identify high-quality exemplars effectively. This deficiency hampers scalability across diverse classes and undermines the development of strong visual associations between the identified classes and image content. To this end, we propose the Visual Association-based Zero-shot Object Counting (VA-Count) framework. VA-Count consists of an Exemplar Enhancement Module (EEM) and a Noise Suppression Module (NSM) that synergistically refine the process of class exemplar identification while minimizing the consequences of incorrect object identification. The EEM utilizes advanced vision-language pretaining models to discover potential exemplars, ensuring the framework's adaptability to various classes. Meanwhile, the NSM employs contrastive learning to differentiate between optimal and suboptimal exemplar pairs, reducing the negative effects of erroneous exemplars. VA-Count demonstrates its effectiveness and scalability in zero-shot contexts with superior performance on two object counting datasets.