Work

A multimodal agent for ceramic management via LLM-driven knowledge and visual synergy

Year: 2026

Type: article

Abstract: Ceramic heritage digitization has produced heterogeneous textual, visual, and structured data, while existing frameworks remain limited in structured extraction, fine-grained visual grounding, and evidence-based reasoning. Therefore, a multimodal ceramic analysis agent (CeramicAgent) is proposed, which integrates schema-constrained information extraction, high-quality visual segmentation based on the High-Quality Segment Anything Model (HQ-SAM), and graph-guided reasoning that connects textual, visual, and historical evidence. Experiments on a Palace Museum ceramic dataset show improved attribute extraction, reliable localization of micro-features, and coherent answers grounded in retrieved knowledge paths. The results indicate that the coordinated use of language, vision, and structured knowledge can support expert-oriented analysis of ancient ceramics, particularly when interpretation depends on degraded surface evidence, specialized terminology, and uncertain historical relations.

Source: npj Heritage Science

DOI: 10.1038/s40494-026-02868-y

Authors: Yuexin Huang, Bin Du, Jingyan Qin

Institutions: University of Science and Technology Beijing, Northwestern Polytechnical University

Language: English

Topic: Image Processing and 3D Reconstruction

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Fundamental Research Funds for the Central Universities

Award: G2374761822