通过留一法分析，从结构角度评估SVG生成质量。
Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis
- 用留一法逐个移除元素，分析其对输出的影响。
- 提出纯度、覆盖率等四类结构指标，量化模块化程度。
- 适合关注SVG可编辑性与结构合理性的研究者。
SVG生成通常通过渲染结果与参考图像对比来评估，这仅反映视觉相似性，却忽略了使SVG可编辑、可分解和可重用的结构特性。受经典刀切法启发，我们引入元素级留一法（LOO）分析：分别渲染包含与不包含每个元素的SVG，从而获得用于质量评估与结构分析的元素级信号。基于此单一机制，我们推导出：(i) 元素级质量评分，实现零样本缺陷检测；(ii) 通过LOO特征与VLM引导的概念热图交叉，实现元素-概念归因；(iii) 四类结构指标：纯度、覆盖率、紧凑性与局部性，从不同角度量化SVG的模块化水平。这些指标将SVG评估从图像相似性扩展至代码结构层面，支持元素级诊断与比较视觉概念在代码中的表示、划分与组织方式。其实际价值在超过19,000次编辑（5种类型）覆盖5个生成系统与3个复杂度层级的数据集上得到验证。
原文摘要 · Abstract (English)
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.
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