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<p>Chinese porcelain holds immense historical and cultural value, making its accurate classification essential for archaeological research and cultural heritage preservation. Traditional classification methods rely heavily on expert analysis, which is time-consuming, subjective, and difficult to scale. This paper explores the application of Deep Learning (DL) and transfer learning techniques to automate the classification of porcelain artifacts across four key attributes: dynasty, glaze, ware, and type. We evaluate four Convolutional Neural Networks (CNNs) - ResNet50, MobileNetV2, VGG16, and InceptionV3 - comparing their performance with and without pre-trained weights. MobileNetV2 achieves the best average performance (93.5% accuracy), followed closely by ResNet50 (92.875%) and InceptionV3 (92.725%), while VGG16 underperforms (84.3%). Generally, the models achieve their highest accuracy in dynasty classification (96.775% on average), whereas glaze and ware are slightly different tasks (92.8% and 92.65%), and type classification proves most challenging (81.175% on average). Results demonstrate that transfer learning consistently improves classification accuracy, with overall gains until 12% across tasks. Furthermore, we incorporate eXplainable AI (XAI) techniques, including Grad-CAM and a SHAP-inspired framework, to reveal the visual cues driving model decisions and to ensure interpretability in cultural heritage applications.</p>

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Multi-task learning for identification of porcelain in Song and Yuan dynasties

  • 期刊论文

  • 发表日期：

    2026年2月5日

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  • Ziyao Ling,
  • Giovanni Delnevo,
  • Paola Salomoni,
  • Silvia Mirri

摘要

Chinese porcelain holds immense historical and cultural value, making its accurate classification essential for archaeological research and cultural heritage preservation. Traditional classification methods rely heavily on expert analysis, which is time-consuming, subjective, and difficult to scale. This paper explores the application of Deep Learning (DL) and transfer learning techniques to automate the classification of porcelain artifacts across four key attributes: dynasty, glaze, ware, and type. We evaluate four Convolutional Neural Networks (CNNs) - ResNet50, MobileNetV2, VGG16, and InceptionV3 - comparing their performance with and without pre-trained weights. MobileNetV2 achieves the best average performance (93.5% accuracy), followed closely by ResNet50 (92.875%) and InceptionV3 (92.725%), while VGG16 underperforms (84.3%). Generally, the models achieve their highest accuracy in dynasty classification (96.775% on average), whereas glaze and ware are slightly different tasks (92.8% and 92.65%), and type classification proves most challenging (81.175% on average). Results demonstrate that transfer learning consistently improves classification accuracy, with overall gains until 12% across tasks. Furthermore, we incorporate eXplainable AI (XAI) techniques, including Grad-CAM and a SHAP-inspired framework, to reveal the visual cues driving model decisions and to ensure interpretability in cultural heritage applications.