Abstract:SVG generation is typically evaluated by comparing rendered outputs to reference images, which captures visual similarity but not the structural properties that make SVG editable, decomposable, and reusable. Inspired by the classical jackknife, we introduce element-level leave-one-out (LOO) analysis. The procedure renders the SVG with and without each element, which yields element-level signals for quality assessment and structural analysis. From this single mechanism, we derive (i) per-element quality scores that enable zero-shot artifact detection; (ii) element-concept attribution via LOO footprints crossed with VLM-grounded concept heatmaps; and (iii) four structural metrics: purity, coverage, compactness, and locality, which quantify SVG modularity from complementary angles. These metrics extend SVG evaluation from image similarity to code structure, enabling element-level diagnosis and comparison of how visual concepts are represented, partitioned, and organized within SVG code. Their practical relevance is validated on over 19,000 edits (5 types) across 5 generation systems and 3 complexity tiers.
| Subjects: | Machine Learning (cs.LG); Applications (stat.AP) |
| Cite as: | arXiv:2604.08809 [cs.LG] |
| (or arXiv:2604.08809v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.08809 arXiv-issued DOI via DataCite |
From: Haonan Zhu [view email]
[v1]
Thu, 9 Apr 2026 22:50:41 UTC (3,060 KB)
[v2]
Fri, 17 Apr 2026 17:54:12 UTC (3,270 KB)
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