Does it Really Count? Assessing Semantic Grounding in Text-Guided Class-Agnostic Counting

Giacomo Pacini; Luca Ciampi; Nicola Messina; Nicola Tonellotto; Giuseppe Amato; Fabrizio Falchi

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arXiv · 2605.02752

Does it Really Count? Assessing Semantic Grounding in Text-Guided Class-Agnostic Counting

Giacomo Pacini·Luca Ciampi·Nicola Messina·Nicola Tonellotto·Giuseppe Amato·Fabrizio Falchi

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Abstract

Open-world text-guided class-agnostic counting (CAC) has emerged as a flexible paradigm for counting arbitrary object classes by using natural language prompts. However, current evaluation protocols primarily focus on standard counting errors within single-category images, overlooking a fundamental requirement: the ability to correctly ground the textual prompt in the visual scene. In this paper, we show that several state-of-the-art CAC models often struggle to determine which object class should be counted based on the given prompt, revealing a misalignment between textual semantics and visual object representations. This limitation leads to spurious counting responses and reduced reliability in real-world scenarios. To systematically address these limitations, we propose a new evaluation framework focused on model robustness and trustworthiness. Our contribution is two-fold: (i) we introduce PrACo++ (Prompt-Aware Counting++), a novel test suite featuring two dedicated evaluation protocols -- the negative-label test and the distractor test -- paired with new specialized metrics; and (ii) we present the MUCCA (MUlti-Category Class-Agnostic counting) evaluation dataset, a new collection of real-world images featuring multiple annotated object categories per scene, unlike existing CAC benchmarks that typically include a single category per image. Our extensive experimental evaluation of 10 state-of-the-art methods shows that, despite strong performance under standard counting metrics, current models exhibit significant weaknesses in understanding and grounding object class descriptions. Finally, we provide a quantitative analysis of how semantic similarity between prompts influences these failures. Overall, our results underscore the need for more semantically grounded architectures and offer a reliable framework for future assessment in open-world text-guided CAC methods.

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Giacomo Pacini, Luca Ciampi, Nicola Messina, Nicola Tonellotto, Giuseppe Amato, Fabrizio Falchi. 2026-05-13. Does it Really Count? Assessing Semantic Grounding in Text-Guided Class-Agnostic Counting. https://arxiv.org/abs/2605.02752

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