KamonBench：一种基于语法规则的数据集，用于评估视觉-语言模型中的组合因子恢复
KamonBench: A Grammar-Based Dataset for Evaluating Compositional Factor Recovery in Vision-Language Models
AI总结：
KamonBench通过20000个合成复合徽章及辅助组件示例，提供评估视觉-语言模型中稀疏组合识别和因子恢复的可控测试环境，支持程序代码因子度量和可控因子对重组。
摘要：
KamonBench通过20000个合成复合徽章及辅助组件示例，提供评估视觉-语言模型中稀疏组合识别和因子恢复的可控测试环境，支持程序代码因子度量和可控因子对重组。
英文摘要：
Kamon (family crests) are an important part of Japanese culture and a natural test case for compositional visual recognition: each crest combines a small number of symbolic choices, but the space of possible descriptions is sparse. We introduce KamonBench, a grammar-based image-to-structure benchmark with 20,000 synthetic composite crests and auxiliary component examples. Each composite crest is paired with a formal kamon description language - "kamon yōgo" - description, a segmented Japanese analysis, an English translation, and a non-linguistic program code. Because each synthetic crest is generated from known factors, namely container, modifier, and motif, KamonBench supports evaluation beyond caption-level accuracy: direct program-code factor metrics, controlled factor-pair recombination splits, counterfactual motif-sensitivity groups under fixed container-modifier contexts, and linear probes of factor accessibility. We include baseline results for a ViT encoder/Transformer decoder and two VGG n-gram decoders, with and without learned positional masks. KamonBench therefore provides a controlled testbed for sparse compositional visual recognition and factor recovery in vision-language models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
