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

arXiv: https://arxiv.org/abs/2303.15266 · HTML: https://mlsanity.com/paper/2303.15266/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 72 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
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- [Bilinear CNNs for Fine-grained Visual Recognition](https://mlsanity.com/paper/1504.07889/) (2015)
- [Learning Deep Representations of Fine-grained Visual Descriptions](https://mlsanity.com/paper/1605.05395/) (2016)
- [Fine-Grained Image Analysis with Deep Learning: A Survey](https://mlsanity.com/paper/2111.06119/) (2021)
- [DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images](https://mlsanity.com/paper/1901.07973/) (2019)
- [Part-based R-CNNs for Fine-grained Category Detection](https://mlsanity.com/paper/1407.3867/) (2014)
- [Learning to Navigate for Fine-grained Classification](https://mlsanity.com/paper/1809.00287/) (2018)
- [Maximum-Entropy Fine-Grained Classification](https://mlsanity.com/paper/1809.05934/) (2018)
- [Coherent Hierarchical Multi-Label Classification Networks](https://mlsanity.com/paper/2010.10151/) (2020)
- [The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification](https://mlsanity.com/paper/2002.04264/) (2020)
- [Part-Stacked CNN for Fine-Grained Visual Categorization](https://mlsanity.com/paper/1512.08086/) (2015)
- [Cross-X Learning for Fine-Grained Visual Categorization](https://mlsanity.com/paper/1909.04412/) (2019)
- [Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition](https://mlsanity.com/paper/1806.05372/) (2018)
- [Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset](https://mlsanity.com/paper/2004.12276/) (2020)
- [Fine-graind Image Classification via Combining Vision and Language](https://mlsanity.com/paper/1704.02792/) (2017)
- [Products-10K: A Large-scale Product Recognition Dataset](https://mlsanity.com/paper/2008.10545/) (2020)
- [Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification](https://mlsanity.com/paper/2201.03194/) (2022)
- [Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding](https://mlsanity.com/paper/1808.04505/) (2018)
- [RPC: A Large-Scale Retail Product Checkout Dataset](https://mlsanity.com/paper/1901.07249/) (2019)
- [Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop](https://mlsanity.com/paper/1512.05227/) (2015)
- [Attribute-Aware Attention Model for Fine-grained Representation Learning](https://mlsanity.com/paper/1901.00392/) (2019)
- [Fine-Grained Object Classification via Self-Supervised Pose Alignment](https://mlsanity.com/paper/2203.15987/) (2022)
- [Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition](https://mlsanity.com/paper/1807.00505/) (2018)
- [Attend in groups: a weakly-supervised deep learning framework for learning from web data](https://mlsanity.com/paper/1611.09960/) (2016)

## Built on by (most cited first, 4 of 4)

- [HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification](https://mlsanity.com/paper/2411.07660/) (2024)
- [Towards Ancient Plant Seed Classification: A Benchmark Dataset and Baseline Model](https://mlsanity.com/paper/2512.18247/) (2025)
- [Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification](https://mlsanity.com/paper/2509.24181/) (2025)
- [Semantics-Aware Hierarchical Consensus Learning for Remote Sensing Image Classification](https://mlsanity.com/paper/2510.04916/) (2025)

## Abstract (arXiv)

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.