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<p>The analysis and protection of grotto sculptures face growing urgency due to climate-driven deterioration and a shortage of domain experts. Here we present ChronoStyleNet (CSN) the world’s first domain-specific multimodal large model purpose-built for sculptural heritage. CSN is trained on 295 expert-annotated statues from the Feilai Peak Grottoes (covering 49% of the West Lake region and 13% of Zhejiang grotto sculptures) and 2.46 GB of archaeological literature. It achieves precise performance through targeted fine-tuning and structured prompting under limited data conditions. Evaluated on 22 Yuan-dynasty samples, CSN outperformed five mainstream multimodal large language systems within a six-dimensional ontology-aligned framework. This work establishes a scalable benchmark for domain-adaptive AI in cultural heritage, offering replicable methodology for other endangered monuments worldwide. CSN demonstrates that domain-adapted multimodal AI can deliver high-precision interpretation even with scarce data, providing new tools for digital preservation and scholarly research. It highlights AI’s potential to bridge expertise gaps and reshape conservation practices in vulnerable heritage contexts.</p>

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Multimodal AI for Yuan Buddhist sculpture chronology and style

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月5日

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  • Jia Xing,
  • Wei Ren,
  • Du Lei,
  • Lin Zhao,
  • Xue Qin,
  • Hongdang Shao,
  • Wenjie Li,
  • Yirui Han,
  • Zike Yu,
  • Zheng Xu,
  • Rui Yin,
  • Jiantao Yuan,
  • Jun Wang,
  • Wei Chen,
  • Jun Xu,
  • Xiaoping Zhou,
  • Cheng Yang,
  • Wei Zhou,
  • Binbin Zhou

摘要

The analysis and protection of grotto sculptures face growing urgency due to climate-driven deterioration and a shortage of domain experts. Here we present ChronoStyleNet (CSN) the world’s first domain-specific multimodal large model purpose-built for sculptural heritage. CSN is trained on 295 expert-annotated statues from the Feilai Peak Grottoes (covering 49% of the West Lake region and 13% of Zhejiang grotto sculptures) and 2.46 GB of archaeological literature. It achieves precise performance through targeted fine-tuning and structured prompting under limited data conditions. Evaluated on 22 Yuan-dynasty samples, CSN outperformed five mainstream multimodal large language systems within a six-dimensional ontology-aligned framework. This work establishes a scalable benchmark for domain-adaptive AI in cultural heritage, offering replicable methodology for other endangered monuments worldwide. CSN demonstrates that domain-adapted multimodal AI can deliver high-precision interpretation even with scarce data, providing new tools for digital preservation and scholarly research. It highlights AI’s potential to bridge expertise gaps and reshape conservation practices in vulnerable heritage contexts.