English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Multi-Granularity Archaeological Dating of Chinese Bronze Dings Based on a Knowledge-Guided Relation Graph