Scholarlink

<p>The use of AI systems for managing large-scale cultural heritage artifacts has become possible due to the rise of digitization. To classify such content, machine learning is typically used, where contextual information is important to structure the data. One way to capture context is through a knowledge graph. In this study, we propose a newd graph neural networks, we can improve artwork classification by utilizing the relationships between entities of the knowledge graph. Our experiments demonstrate that this approach achieves state-of-the-art results on multiple classification tasks across three datasets (SemArt paintings, Buddha statues, and Ukiyo-e woodblock prints). Moreover, our approach is effective in dealing with unbalanced data and we explore the use of both graph attention mechanisms and focal loss functions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

GNNBoost: boosting artwork classification with graph embeddings

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月25日

查看全文
  • Cheikh Brahim El Vaigh,
  • Noa Garcia,
  • Benjamin Renoust,
  • Chenhui Chu,
  • Yuta Nakashima,
  • Yiming Qian,
  • Hajime Nagahara

摘要

The use of AI systems for managing large-scale cultural heritage artifacts has become possible due to the rise of digitization. To classify such content, machine learning is typically used, where contextual information is important to structure the data. One way to capture context is through a knowledge graph. In this study, we propose a newd graph neural networks, we can improve artwork classification by utilizing the relationships between entities of the knowledge graph. Our experiments demonstrate that this approach achieves state-of-the-art results on multiple classification tasks across three datasets (SemArt paintings, Buddha statues, and Ukiyo-e woodblock prints). Moreover, our approach is effective in dealing with unbalanced data and we explore the use of both graph attention mechanisms and focal loss functions.