A Computational Approach to Visual Metonymy

Saptarshi Ghosh; Linfeng Liu; Tianyu Jiang

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arXiv · 2601.17706

A Computational Approach to Visual Metonymy

Saptarshi Ghosh·Linfeng Liu·Tianyu Jiang

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Abstract

Images often communicate more than they literally depict: a set of tools can suggest an occupation and a cultural artifact can suggest a tradition. This kind of indirect visual reference, known as visual metonymy, invites viewers to recover a target concept via associated cues rather than explicit depiction. In this work, we present the first computational investigation of visual metonymy. We introduce a novel pipeline grounded in semiotic theory that leverages large language models and text-to-image models to generate metonymic visual representations. Using this framework, we construct ViMET, the first visual metonymy dataset comprising 2,000 multiple-choice questions to evaluate the cognitive reasoning abilities in multimodal language models. Experimental results on our dataset reveal a significant gap between human performance (86.9%) and state-of-the-art vision-language models (65.9%), highlighting limitations in machines' ability to interpret indirect visual references. Our dataset is publicly available at: https://github.com/cincynlp/ViMET.

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Saptarshi Ghosh, Linfeng Liu, Tianyu Jiang. 2026-01-25. A Computational Approach to Visual Metonymy. https://arxiv.org/abs/2601.17706

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