TY - CPAPER
KW - Adversarial networks
KW - Chinese paper cutting
KW - Computer Vision
KW - Cutting
KW - Cutting patterns
KW - Digital protection
KW - Historic preservation
KW - Intangible cultural heritages
KW - Mean square error
KW - Optimization algorithms
KW - Paper cutting
KW - Progressive conditional generative adversarial network
KW - Realistic sample
KW - Vision transformer
AU - Hanwen Fan
AU - Xiaomeng Zhang
AB - Since 21st Century, Chinese paper cutting has been recognized as an important element of Intangible Cultural Heritage (ICH) of China, which served as a symbol of traditional aesthetics, cultural identity and social values. Due to their rich cultural importance, the digital protection of paper cutting artwork is crucial. Moreover, existing approaches such as Progressive Conditional Generative Adversarial Network with Archimedes Optimization Algorithm (PCGAN-AOA) struggle from generalizability, due to lack of ability to capture long range dependencies in paper cutting patterns. Hence, A hybrid novel approach namely, PCGAN with Vision Transformer (PCGAN-ViT) is proposed for digital protection of ICH and inheritance of Chinese paper cutting. Initially, the input data consists of complete paper cutting pattern images which are gathered from multiple sources such as local cultural heritage institutions, museums, and verified public repositories. Then these images are preprocessed by performing resizing through bilinear interpolation and normalization of pixels through min max scaling. Lastly, these preprocessed images are fed to the proposed PCGAN-ViT model, where PCGAN performs the generation of realistic samples and ViT is used to verify the stylistic correctness by classifying the generated patterns. Therefore, the proposed PCGAN-ViT achieved low Root Mean Squared Error (RMSE) of 0.097 which outperformed existing PCGAN-AOA model.
DO - 10.1109/ICDSIS65355.2025.11071120
N1 - Type: Conference paper
N2 - Since 21st Century, Chinese paper cutting has been recognized as an important element of Intangible Cultural Heritage (ICH) of China, which served as a symbol of traditional aesthetics, cultural identity and social values. Due to their rich cultural importance, the digital protection of paper cutting artwork is crucial. Moreover, existing approaches such as Progressive Conditional Generative Adversarial Network with Archimedes Optimization Algorithm (PCGAN-AOA) struggle from generalizability, due to lack of ability to capture long range dependencies in paper cutting patterns. Hence, A hybrid novel approach namely, PCGAN with Vision Transformer (PCGAN-ViT) is proposed for digital protection of ICH and inheritance of Chinese paper cutting. Initially, the input data consists of complete paper cutting pattern images which are gathered from multiple sources such as local cultural heritage institutions, museums, and verified public repositories. Then these images are preprocessed by performing resizing through bilinear interpolation and normalization of pixels through min max scaling. Lastly, these preprocessed images are fed to the proposed PCGAN-ViT model, where PCGAN performs the generation of realistic samples and ViT is used to verify the stylistic correctness by classifying the generated patterns. Therefore, the proposed PCGAN-ViT achieved low Root Mean Squared Error (RMSE) of 0.097 which outperformed existing PCGAN-AOA model.
TI - Digital Protection and Inheritance of Intangible Cultural Heritage using Progressive Conditional Generative Adversarial Network with Vision Transformer
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011827817&doi=10.1109%2FICDSIS65355.2025.11071120&partnerID=40&md5=6e354837a4675343fa6dda6a1e25cdde
ER -