VisTCP: A Visualization Framework to Construct Knowledge-Graph-Based Representation for Traditional Chinese Painting
arXiv:2607.05841v1 Announce Type: cross Abstract: Structured representation can characterize semantic objects and relationships in images. It provides a possible effective way for the semantic understanding of Traditional Chinese Paintings (TCPs) to better support archaeology and art history research. However, most image-oriented structured representation methods perform poorly on TCPs, due to two major challenges: 1) the objects and events of TCPs exhibit substantial differences from modern natural images, which results in semantic misunderstandings of TCPs; and 2) it is difficult to achieve
The proliferation of AI and advanced computer vision techniques is enabling new applications in cultural heritage and art history research, even for previously challenging domains like Traditional Chinese Paintings.
This development allows for better structured representation and semantic understanding of complex cultural artifacts, which can revolutionize archaeological and art historical analysis previously limited by semantic ambiguities.
Art-historical and archaeological research of Traditional Chinese Paintings could achieve new levels of depth and accuracy through AI-powered knowledge graph representations, overcoming prior limitations in object and event interpretation.
- · Art historians
- · Archaeologists
- · Cultural preservation institutions
- · AI/ML researchers in cultural heritage
- · Traditional manual cataloging methods
AI models gain enhanced capabilities for analyzing and interpreting complex artistic and cultural data.
New digital archives and interactive tools emerge for studying and preserving art forms that were previously difficult to digitize semantically.
This could set a precedent for AI-driven understanding of other non-Western or historically distinct visual cultural assets, broadening global AI application in humanities.
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Read at arXiv cs.AI