An efficient low-shot class-agnostic counting framework with hybrid encoder and iterative exemplar feature learning

Qinghua Yang, Bin Liu, Yan Tian, Yangming Shi, Xinxin Du, Fangyuan He and Jikun Guo

PLOS ONE, 2025, vol. 20, issue 6, 1-25

Abstract: Few-shot learning techniques have enabled the rapid adaptation of a general AI model to various tasks using limited data. In this study, we focus on class-agnostic low-shot object counting, a challenging problem that aims to achieve accurate object counting with only a few annotated samples (few-shot) or even in the absence of any annotated data (zero-shot). In existing methods, the primary focus is often on enhancing performance, while relatively little attention is given to inference time—an equally critical factor in many practical applications. We propose a model that achieves real-time inference without compromising performance. Specifically, we design a multi-scale hybrid encoder to enhance feature representation and optimize computational efficiency. This encoder applies self-attention exclusively to high-level features and cross-scale fusion modules to integrate adjacent features, reducing training costs. Additionally, we introduce a learnable shape embedding and an iterative exemplar feature learning module, that progressively enriches exemplar features with class-level characteristics by learning from similar objects within the image, which are essential for improving subsequent matching performance. Extensive experiments on the FSC147, Val-COCO, Test-COCO, CARPK, and ShanghaiTech datasets demonstrate our model’s effectiveness and generalizability compared to state-of-the-art methods.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0322360

DOI: 10.1371/journal.pone.0322360

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