用深度学习自动识别宋元瓷器朝代、釉色等属性，提升文物分类效率与准确性。
Multi-task Learning for Identification of Porcelain in Song and Yuan Dynasties
- 采用迁移学习和四种CNN模型，实现瓷器多属性自动分类。
- 使用预训练权重的MobileNetV2和ResNet50准确率显著高于从零训练的模型。
- 适合文化遗产保护、考古研究及数字文博领域的技术应用。
中国瓷器具有极高的历史与文化价值，其精准分类对考古研究和文化遗产保护至关重要。传统分类依赖专家分析，耗时长、主观性强且难以扩展。本文探索深度学习与迁移学习在宋元时期瓷器四类关键属性（朝代、釉色、窑口、类型）分类中的应用。评估了四种卷积神经网络（ResNet50、MobileNetV2、VGG16、InceptionV3），对比其是否使用预训练权重的表现。结果表明，迁移学习显著提升分类精度，尤其在类型分类这类复杂任务中，从零训练的模型表现较差。MobileNetV2和ResNet50在所有任务中均表现出高准确率与鲁棒性，而VGG16在多样化分类中表现不佳。文中还讨论了数据集局限性，并提出未来方向：领域特定预训练、注意力机制融合、可解释AI方法，以及向其他文化遗物泛化。
原文摘要 · Abstract (English)
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.
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