TY - CPAPER
KW - AIGC
KW - Arts computing
KW - Contrastive Learning
KW - Globalisation
KW - Innovative approaches
KW - Intangible cultural heritages
KW - Learning systems
KW - machine learning
KW - Machine learning models
KW - Machine-learning
KW - Model construction
KW - Neural networks
KW - Paper cutting
KW - Quantitative evaluation
KW - Trajectories
KW - Trajectory analysis
AU - Ziyi Wang
AU - Yong Liu
AB - With the acceleration of globalization, intangible cultural heritage (ICH) faces new challenges, particularly in the crisis of craftsmanship inheritance. Effectively quantifying and assessing artisans  skills is crucial for preserving traditional craftsmanship. Therefore, this study proposes an innovative approach based on papercutting art, utilizing stroke trajectory analysis combined with machine learning models to quantitatively evaluate the skills of papercutting inheritors. Specifically, high-quality stroke trajectory images of papercutting art are collected using web crawling techniques. Degraded images are then generated through threshold processing and pixel swapping, followed by the use of a diffusion model to synthesize defective stroke trajectory images. Based on these data, we design the BruNet model, which extracts multi-space features through convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs). These features are then fused using a feature fusion module and trained with cross-entropy and contrastive learning losses. Experimental results demonstrate that BruNet achieves significant performance improvements over state-of-the-art models, providing novel insights and technical support for ICH preservation.
DO - 10.1109/AIITA65135.2025.11048123
N1 - Type: Conference paper
N2 - With the acceleration of globalization, intangible cultural heritage (ICH) faces new challenges, particularly in the crisis of craftsmanship inheritance. Effectively quantifying and assessing artisans  skills is crucial for preserving traditional craftsmanship. Therefore, this study proposes an innovative approach based on papercutting art, utilizing stroke trajectory analysis combined with machine learning models to quantitatively evaluate the skills of papercutting inheritors. Specifically, high-quality stroke trajectory images of papercutting art are collected using web crawling techniques. Degraded images are then generated through threshold processing and pixel swapping, followed by the use of a diffusion model to synthesize defective stroke trajectory images. Based on these data, we design the BruNet model, which extracts multi-space features through convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs). These features are then fused using a feature fusion module and trained with cross-entropy and contrastive learning losses. Experimental results demonstrate that BruNet achieves significant performance improvements over state-of-the-art models, providing novel insights and technical support for ICH preservation.
SP - 1332
EP - 1336
TI - Quantitative Evaluation of Intangible Cultural Heritage Inheritance: Stroke Trajectory Analysis and Machine Learning Model Construction for Paper-Cutting Craftsmanship
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-105012115661&doi=10.1109%2FAIITA65135.2025.11048123&partnerID=40&md5=48e68ddb5558db71ac17c698d1a91dfe
ER -