Abstract Generating authoritative and contextually rich captions for Chinese cultural relics remains a significant challenge for standard vision-language models due to the specialized terminology and deep historical context required. We propose a novel, multi-stage retrieval-augmented generation framework designed to bridge the gap between visual identification and expert-level documentation. Our pipeline first utilizes a contrastive language-image pre-training-based encoder to map artifact images into a high-level semantic space, providing initial linguistic anchors. These anchors serve as queries for a tiered knowledge retrieval system that extracts fine-grained, domain-specific information from a curated repository of Chinese cultural heritage. To ensure factual integrity, the framework synthesizes these multi-source inputs into a unified text knowledge vector, which is integrated with visual features through a late-fusion multi-layer perceptron adapter. This aligned multimodal representation is then processed by a large language model optimized via low-rank adaptation to produce comprehensive, culturally grounded captions. Experimental results demonstrate that our framework significantly outperforms state-of-the-art baselines in both automatic metrics and human expert evaluations, effectively mitigating hallucinations and providing a scalable solution for digital museology.
Mi et al. (2026) studied this question.