The paper introduces VMetaphor-Bench, a benchmark for evaluating visual metaphor generation in text-to-image models, comprising 1,500 curated metaphors across three levels and ten categories, each paired with two prompts of varying specificity. It proposes a hybrid evaluation framework using a multiple-choice question protocol and dimension-based scoring to assess metaphorical fidelity. Experiments on 11 T2I models show that even top proprietary models struggle with compositional structuring and cross-domain mapping, underscoring the need for further research in this area.
By Chuer Chen, Zichen Wang, Yi He, Zhengxi Yu, Nan Cao
Similes provide a compact and expressive way to describe visual characteristics in text prompts. Recent text-to-image models (t2i models) can produce visually compelling outputs from simile prompts, yet even frontier models frequently misinterpret the metaphorical vehicle and confuse it with the object.
The paper introduces Visual Metaphor Transfer (VMT), a task that requires models to extract the abstract ‘creative essence’ from a reference image and apply it to a new target subject. It proposes a multi‑agent framework based on Conceptual Blending Theory, using a Schema Grammar to separate relational invariants from visual entities. The system includes perception, transfer, generation, and diagnostic agents, and experimental results show it outperforms state‑of‑the‑art baselines in metaphor consistency, analogy appropriateness, and visual creativity.
By Yu Xu, Yuxin Zhang, Lin Gao, Oliver Deussen, Tong-Yee Lee, Fan Tang
arXiv:2608. 15828v1 Announce Type: cross Abstract: Current evaluation of metaphor explanations relies mainly on holistic quality ratings, revealing little about how explanation quality is structured or where human judgments agree and diverge.
By Ana Naveriani, Jakob Suchan, Stefano Zoia, Mehul Bhatt, Antonio Lieto, Gian Luca Pozzato
arXiv:2606. 28406v1 Announce Type: new Abstract: Text-to-image and multimodal generative models are increasingly used to produce scientific figures such as mechanism diagrams, experimental-design schematics, conceptual frameworks, and graphical abstracts.
By Davie Chen
arXiv:2510. 04120v2 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such behavioral success reveals about metaphor processing.
By Fengying Ye, Shanshan Wang, Lidia S. Chao, Derek F. Wong