Zero-shot Object Counting with Good Exemplars
Huilin Zhu, Jingling Yuan, Zhengwei Yang, Yu Guo, Zheng Wang, Xian, Zhong, Shengfeng He

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…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Advanced Neural Network Applications · Image Retrieval and Classification Techniques
MethodsContrastive Learning
