mlsanityData to 7 Oct 2026

Presence Is Not Faithfulness: Figurative Vehicle Intrusion in Text-to-Image Generation

arXiv 2609.32188 ↗First posted 26 Sep 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Text-to-image (TTI) models increasingly generate high-quality images from natural-language prompts, yet figurative language exposes a failure: a vehicle that should guide the depiction of a tenor may instead be rendered as a visible object. We call this failure Figurative Vehicle Intrusion: the intruding content is textually licensed, but it is assigned the wrong visual role, showing that visual presence is not always faithfulness and that presence-oriented evaluation can miss such errors. To study it systematically, we introduce Vehicle Intrusion and Semantic Tenor Assessment (VISTA), a multilingual benchmark of figurative prompts organized by Figurative Form and Mapping Mechanism. We further propose V-Score, a diagnostic question-answering metric that evaluates role-aware figurative faithfulness in generated images. Evaluations on recent high-performing TTI models show that vehicle intrusion persists across languages and figurative categories. As a lightweight mitigation, we introduce VISTA-Guard, which partially reduces vehicle intrusion and suggests a practical path toward more figuratively faithful TTI generation. All resources will be released publicly.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 47 references that are in the map. First posted on arXiv 26 Sep 2026.

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References · 24

  1. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  2. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  3. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,094
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  5. 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,388
  6. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
  7. 2023ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generationcited by 1,010
  8. 2023Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generationcited by 950
  9. 2023GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignmentcited by 946
  10. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
  11. 2023TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answeringcited by 416
  12. 2022Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesiscited by 386
12 more
  1. 2024Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMscited by 224
  2. 2023Holistic Evaluation of Text-To-Image Modelscited by 160
  3. 2023Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generationcited by 159
  4. 2023What You See is What You Read? Improving Text-Image Alignment Evaluationcited by 105
  5. 2025Easier Painting Than Thinking: Can Text-to-Image Models Set the Stage, but Not Direct the Play?cited by 33
  6. 2023I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphorscited by 31
  7. 2024Understanding Figurative Meaning through Explainable Visual Entailmentcited by 19
  8. 2024Evaluating Image Hallucination in Text-to-Image Generation with Question-Answeringcited by 15
  9. 2023Translation-Enhanced Multilingual Text-to-Image Generationcited by 9
  10. 2023ViPE: Visualise Pretty-much Everythingcited by 3
  11. 2022Language Does More Than Describe: On The Lack Of Figurative Speech in Text-To-Image Modelscited by 2
  12. 2015Exploring Metaphorical Senses and Word Representations for Identifying Metonymscited by 1

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