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T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation
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We propose T2I-ReasonBench, a benchmark evaluating reasoning capabilities of text-to-image (T2I) models. It consists of four dimensions: Idiom Interpretation, Textual Image Design, Entity-Reasoning and Scientific-Reasoning. We propose a two-stage evaluation protocol to assess the reasoning accuracy and image quality. We benchmark various T2I generation models, and provide comprehensive analysis on their performances.
Forward citations
Cited by 5 Pith papers
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New 3DLP benchmark with real-world 1K HDR pairs shows state-of-the-art image editing models vary in physical lighting consistency, with best models close to reality but error-prone in low-light regions.
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More Than Meets the Eye: Measuring the Semiotic Gap in Vision-Language Models via Semantic Anchorage
Vision-language models exhibit literal superiority bias on noun compounds, with photorealistic visuals linked to poorer idiomatic grounding via new DIVA benchmark and Δ metric.
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SciIR: A Large-scale Training Dataset and Benchmark for Scientific Image Reasoning Generation
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Faithful, Enriched, and Precise: Benchmarking Natural-Science Illustration Generation by T2I models
Introduces FEPBench benchmark to evaluate T2I models on instruction faithfulness, reasoning enrichment, and semantic precision for natural-science illustrations using atom set annotations.
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Qwen-Image-Bench: From Generation to Creation in Text-to-Image Evaluation
Qwen-Image-Bench introduces a hierarchical creator-centric benchmark with 1000 prompts, 23 sub-capabilities, and a Q-Judger model that scores images on 56 verifiable facets to distinguish T2I models on fidelity and cr...
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