TASTE supplies designer multi-dimensional rankings of T2I graphic outputs with statistical validation showing moderate agreement and benchmarks where a TASTE-trained MLP outperforms off-the-shelf VLMs.
LICA: Lay- ered image composition annotations for graphic design research
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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.
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TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design
TASTE supplies designer multi-dimensional rankings of T2I graphic outputs with statistical validation showing moderate agreement and benchmarks where a TASTE-trained MLP outperforms off-the-shelf VLMs.
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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.