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.Comment: CVPR2023 accepte