arXiv cs.AI· Mengyan Wang, Jiyuan An, Yining Liu, YaoWen Zhang, Liner Yang ang Erhong Yang·· 12 小时前AI 评分31
ACVA-Bench：评估文生图模型抽象概念可视化能力的新基准
ACVA-Bench: Beyond Prompt Following in Abstract Concept Visualization Ability for Text-to-Image Models
AI 导读
研究者提出 ACVA-Bench，一个基于心理语言学规范、类别均衡的基准，用于评估文生图模型的抽象概念可视化能力（ACVA），涵盖语义充分性、表征多样性和概念区分度三个维度。
正文 · 原文
Abstract:Text-to-image (T2I) models have achieved remarkable progress in image quality and instruction following, prompting growing research interest in their capacity for deep understanding that goes beyond literal prompt matching. However, without an explicit visual scheme provided, it remains unclear and has not been systematically evaluated whether T2I models can autonomously translate abstract concepts lacking a fixed visual referent, such as "loyalty," into visual representations that are accurate, diverse, and distinguishable from closely related concepts. We define this capability as Abstract Concept Visualization Ability (ACVA) and introduce ACVA-Bench, a category-balanced benchmark grounded in psycholinguistic norms of abstract concepts, which examines models' ACVA along three dimensions semantic sufficiency, representational diversity, and conceptual distinctiveness under a low-intervention setting that withholds concrete objects, scenes, or visual metaphors. Since abstract concepts lack a unique reference image, conventional evaluation metrics that rely on reference-image matching are difficult to apply. To address this, we propose a fine-grained visual-semantic evaluation method constrained by concept semantics, which decomposes concept semantics into independently verifiable visual-semantic questions to assess the semantic representation quality of generated images, with its validity further confirmed by human evaluation. Benchmarking results on mainstream T2I models show that these models rely on a small set of prototypical visual patterns, leaving room for improvement in representational diversity and conceptual distinctiveness for abstract concepts, thereby providing an interpretable and reproducible evaluation foundation for future research.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32058 [cs.CV] |
| (or arXiv:2610.32058v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32058 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiyuan An [view email]
[v1]
Fri, 23 Oct 2026 01:54:37 UTC (6,434 KB)
来源：arXiv cs.AI · arxiv.org