TASTE supplies designer ratings across nine criteria for outputs from four text-to-image models, with statistical tests showing moderate agreement and benchmarks where existing scorers reach at most 0.55 macro agreement while a new head reaches 0.611.
LICA: Layered image composition annotations for graphic design research
2 Pith papers cite this work. Polarity classification is still indexing.
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Element-level leave-one-out analysis yields per-element quality scores and four structural metrics (purity, coverage, compactness, locality) that quantify SVG modularity and enable artifact detection.
citing papers explorer
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TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design
TASTE supplies designer ratings across nine criteria for outputs from four text-to-image models, with statistical tests showing moderate agreement and benchmarks where existing scorers reach at most 0.55 macro agreement while a new head reaches 0.611.
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Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis
Element-level leave-one-out analysis yields per-element quality scores and four structural metrics (purity, coverage, compactness, locality) that quantify SVG modularity and enable artifact detection.