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：青铜鼎的考古断代在古代中国历史研究中扮演着关键角色。当前考古学依赖训练有素的专家进行青铜器断代，这一过程耗时且劳力密集。针对此类断代，本研究提出一种基于学习的方法，将先进深度学习技术与考古知识相融合。为实现此目标，我们首先收集了大规模青铜鼎图像数据集，该数据集包含比现有细粒度数据集更丰富的属性信息。其次，我们引入多头分类器及知识引导关系图，以挖掘属性与鼎时代之间的关联。第三，我们与多种现有方法进行了对比实验，结果表明我们的断代方法达到了最优性能。希望我们的数据与所应用网络能丰富与其他交叉学科领域相关的细粒度分类研究。所用数据集与源代码已包含在补充材料中，并将在投稿后因匿名政策而公开。源代码与数据可访问：https://github.com/zhourixin/bronze-Ding。