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.

翻译：图像所传达的信息往往超出其字面描绘：一组工具可以暗示某种职业，一件文化器物可以暗示某种传统。这种间接的视觉指涉被称为视觉转喻，它引导观者通过关联线索而非直接描绘来理解目标概念。本研究首次对视觉转喻展开计算化探索。我们提出一种基于符号学理论的新型流程，该流程利用大语言模型和文生图模型生成转喻性视觉表征。基于此框架，我们构建了首个视觉转喻数据集ViMET，包含2000道多项选择题，用于评估多模态语言模型的认知推理能力。在该数据集上的实验结果显示：人类表现（86.9%）与前沿视觉语言模型表现（65.9%）存在显著差距，这揭示了机器在理解间接视觉指涉能力上的局限性。本数据集已公开于：https://github.com/cincynlp/ViMET。