The archaeological dating of bronze dings has played a critical role in the study of ancient Chinese history. Current archaeology depends on trained experts to carry out bronze dating, which is time-consuming and labor-intensive. For such dating, in this study, we propose a learning-based approach to integrate advanced deep learning techniques and archaeological knowledge. To achieve this, we first collect a large-scale image dataset of bronze dings, which contains richer attribute information than other existing fine-grained datasets. Second, we introduce a multihead classifier and a knowledge-guided relation graph to mine the relationship between attributes and the ding era. Third, we conduct comparison experiments with various existing methods, the results of which show that our dating method achieves a state-of-the-art performance. We hope that our data and applied networks will enrich fine-grained classification research relevant to other interdisciplinary areas of expertise. The dataset and source code used are included in our supplementary materials, and will be open after submission owing to the anonymity policy. Source codes and data are available at: https://github.com/zhourixin/bronze-Ding.

翻译：青铜鼎的考古定年在中国古代历史研究中起着关键作用。当前的考古学依赖于训练有素的专家进行青铜定年，这一过程耗时费力。针对此类定年，本研究提出了一种基于学习的方法，将先进的深度学习技术与考古知识相结合。为实现这一目标，我们首先收集了一个大规模青铜鼎图像数据集，该数据集比现有的细粒度数据集包含更丰富的属性信息。其次，我们引入了一个多头分类器和一个知识引导的关系图，以挖掘属性与鼎时代之间的关系。第三，我们与多种现有方法进行了对比实验，结果表明我们的定年方法达到了最先进的性能。我们希望我们的数据和所应用的网络能够丰富与其它跨学科专业领域相关的细粒度分类研究。使用的数据集和源代码包含在我们的补充材料中，并将在提交后因匿名政策公开。源代码和数据可在以下网址获取：https://github.com/zhourixin/bronze-Ding。