arXiv cs.AI· Jiahao Huang, Kon Woo Kim, Song-Duo Ma, Yuka Kitamura, Felix Wagner, Yunshan Huang, Sunisth Kumar, Xanh Ho, Yun-Ang Wu, Zhijie Chen, Yunjia Xu, Junfeng Jiang, Zefan Yu, Fei Cheng, Akiko Aizawa·· 1 天前AI 评分39
文本到图像模型的“过度实现”问题：OverReal 基准揭示八款系统均会画出提示词未要求的目标
When Chekhov's Gun Fires Anyway: Over-Realization in Text-to-Image Models
AI 导读
研究者提出“过度实现”（over-realization）概念，指文本到图像模型把提示词提及但未要求呈现的目标画进图像，并构建 OverReal 基准进行测量：OverReal-Gen 含 698 条覆盖否定、信念想象、比喻喻体和隐藏视角四种情境的提示词，OverReal-Det 含 2,841 张人工标注图像用于验证自动评判。
正文 · 原文
Authors:Jiahao Huang, Kon Woo Kim, Song-Duo Ma, Yuka Kitamura, Felix Wagner, Yunshan Huang, Sunisth Kumar, Xanh Ho, Yun-Ang Wu, Zhijie Chen, Yunjia Xu, Junfeng Jiang, Zefan Yu, Fei Cheng, Akiko Aizawa
Abstract:In text-to-image generation, a target that the prompt mentions often appears in the generated image in a way the prompt does not call for. We call this failure over-realization. Mental-spaces theory points to the situations where it can happen: under a negation, inside what a character believes or imagines, as the vehicle of a figurative comparison, or behind a viewpoint that hides the target. Prior work has studied only negation in depth, and the conventional alignment and preference metrics do not penalize the failure. We build OverReal to measure it: OverReal-Gen, 698 prompts covering the four situations, on which we measure each generator's over-realization rate; and OverReal-Det, 2,841 generated images human-labeled for whether and how the target is over-realized, on which we verify the automatic judge that produces this rate. Our evaluation of eight text-to-image systems reveals that all of them over-realize, and that conventional metrics such as CLIPScore reward the failure rather than penalize it. Prompt expansion only partly mitigates the failure. Inside the generator, the diffusion backbone attends to a mentioned target almost as much as to a requested one. The cue words that should hold the target back receive less attention than ordinary words.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.16630 [cs.CV] |
| (or arXiv:2611.16630v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16630 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiahao Huang [view email]
[v1]
Thu, 12 Nov 2026 11:03:25 UTC (2,303 KB)
来源：arXiv cs.AI · arxiv.org