mEOL creates aligned embeddings for text, images, and SVGs using instruction-guided MLLM one-word summaries and semantic SVG rewriting, outperforming baselines on a new text-to-SVG retrieval benchmark.
Visually de- scriptive language model for vector graphics reasoning
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CSL recovers mark type (0.822), visualization role (0.853), and data role (0.860) macro accuracy from 102 SVGs via cohort decomposition and hybrid grounding, outperforming non-cohort baseline.
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mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrieval
mEOL creates aligned embeddings for text, images, and SVGs using instruction-guided MLLM one-word summaries and semantic SVG rewriting, outperforming baselines on a new text-to-SVG retrieval benchmark.
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Cohort-based Semantic Labeling: AI-Enabled Recovery of Visualization Semantics from Deployed SVGs
CSL recovers mark type (0.822), visualization role (0.853), and data role (0.860) macro accuracy from 102 SVGs via cohort decomposition and hybrid grounding, outperforming non-cohort baseline.