arXiv (OAI Expanded)
open access
Blending Concepts: Benchmarking Visual Metaphor Generation in Text-to-Image Models
Text-to-image (T2I) models have achieved remarkable success at faithfully rendering specified objects and attributes, yet their ability to produce visual metaphors, images that convey abstract ideas by combining elements from two distinct domains, remains largely unexamined. To bridge this gap, we i
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Blending Concepts: Benchmarking Visual Metaphor Generation in Text-to-Image Models#924379
- OSPO: Object-Centric Self-Improving Preference Optimization for Text-to-Image Generation#1105911
- GenPuzzle: Benchmarking Visual Reasoning in Image Generation Models#973521
- TPSO: Training-Free Diverse Image Generation via Semantic Prompt Embedding Optimization#799913
- What Comes Next? Omni-StoryBench for Evaluating Story-Grounded Omnimodal Generation#1141360
Record · ID 920148
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive