Exploring Naming Inventories for Architectural Elements for Use in Multi-modal Machine Learning Applications*
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
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.
Details
Original language | English |
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Title of host publication | Computational Methods in the Humanities 2022 (COMHUM 2022) |
Pages | 95-105 |
Number of pages | 11 |
Publication status | Published - 2023 |
Peer-reviewed | Yes |
Externally published | Yes |
Publication series
Series | CEUR Workshop Proceedings |
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Volume | 3602 |
ISSN | 1613-0073 |
Conference
Title | 2nd Workshop on Computational Methods in the Humanities, COMHUM 2022 |
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Duration | 9 - 10 June 2022 |
City | Lausanne |
Country | Switzerland |
External IDs
ORCID | /0000-0002-2456-9731/work/155292070 |
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Keywords
ASJC Scopus subject areas
Keywords
- Architecture, Art History, Computer Vision, Machine Learning