Multi-task Learning for Identification of Porcelain in Song and Yuan Dynasties
This addresses the time-consuming and subjective nature of expert-based porcelain classification for archaeological research and cultural heritage preservation, though it is incremental in applying existing methods to a new domain.
This paper tackles the problem of automating classification of Chinese porcelain artifacts across four attributes (dynasty, glaze, ware, and type) using deep learning and transfer learning, finding that transfer learning significantly enhances accuracy with MobileNetV2 and ResNet50 performing best.
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 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. Our results demonstrate that transfer learning significantly enhances classification accuracy, particularly for complex tasks like type classification, where models trained from scratch exhibit lower performance. MobileNetV2 and ResNet50 consistently achieve high accuracy and robustness across all tasks, while VGG16 struggles with more diverse classifications. We further discuss the impact of dataset limitations and propose future directions, including domain-specific pre-training, integration of attention mechanisms, explainable AI methods, and generalization to other cultural artifacts.