Exploring Naming Inventories for Architectural Elements for Use in Multi-modal Machine Learning Applications
Exploring Naming Inventories for Architectural Elements for Use in Multi-modal Machine Learning Applications
复制标题
探索用于多模式机器学习应用程序的架构元素的命名清单
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Sina Zarrieß
中科院分区：
文献类型：
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作者：
R. Utescher;Aaron Pattee;Ferdinand Maiwald;J. Bruschke;Stephan Hoppe;Sander Münster;Florian Niebling;Sina Zarrieß
计算机视觉模型与数字历史越来越相关和有用。除了日益复杂的神经模型之外，数据和数据选择是这个过程中不可或缺的一部分。在本文中，我们检查并扩展了建筑元素分类领域最近一篇主要论文的数据收集实践。我们收集了 56 个巴洛克地标的图像文本数据集，并以类似的方式进行分析。这种不同的架构领域让我们深入了解原始模型和数据收集过程的可转移性。值得注意的是，架构领域还对架构元素类的可用性以及对它们进行分类的模型的性能产生影响。
Computer vision models are increasingly relevant and useful to Digital History. Next to the increasingly complex neural models, data and data selection are an integral part of this process. In this paper, we examine and extend the data collection practices from a major recent paper in the domain of architectural element classification. We collected an image-text data set for a selection of 56 Baroque landmarks to be analysed in like manner. This different architectural domain yielded insights into the transferability of the original model and data collection procedures. Notably, the architectural domain also has an impact on the availability of classes of architectural elements as well as the performance of the models classifying them.