Parameter-efficient cross-modal prompt tuning for few-shot ancient mural classification

Abstract

Ancient murals are invaluable cultural heritage, and their automatic classification is crucial for digital preservation, stylistic analysis, and heritage management. Existing approaches require large, high-quality annotated datasets, but such expert annotations are costly and often unavailable. To address this, we formulate a few-shot ancient mural classification problem and propose a Parameter-Efficient Cross-modal Prompt Tuning (PE-CPT) framework. Specifically, PE-CPT adapts a pre-trained vision-language model by learning a small set of prompt vectors without updating its original parameters. It performs cross-modal prompt tuning between image and text encoders, generating image prompts from text prompts to maintain semantic consistency. To further improve adaptation, we incorporate LoRA, a parameter-efficient adaptation method applied to attention and feed-forward layers. Experiments in a 16-shot setting demonstrate that PE-CPT outperforms state-of-the-art baselines, achieving improvements of 6.79%, 6.59%, and 7.07% in accuracy, precision, and F1 score. These results demonstrate its effectiveness in cultural heritage analysis under data scarcity.