Processing History
| dc.contributor.author | Perera, Walpola Layantha | |
| dc.contributor.author | Messemer, Heike | |
| dc.contributor.author | Clados, Christiane | |
| dc.date.accessioned | 2023-04-06T15:11:46Z | |
| dc.date.available | 2023-04-06T15:11:46Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | The digital preservation of cultural heritage is an important and challenging task for the research community. Reconstructing historical objects, which do not exist anymore, in the form of digital 3D models makes it possible to visualize them and present them to the public. The reconstruction process as well as the visualization lead to a deeper understanding of the lost historical objects. But the process of the digitalreconstruction is complex and time consuming as diverse sources have to be consulted and interpreted. Therefore, in this paper the latest technology in the feld of artifcial intelligence (AI) is used to support researchers in the feld of Digital Humanities: A Transformer deep learning model based on questions answering methods is introduced to assist to digitally reconstruct historical objects in 3D. It implies a new dimension of data availability, which supports the knowledge process by making large amounts of data qualitatively accessible. [Aus: Einleitung] | en |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/41158 | |
| dc.language.iso | en | |
| dc.publisher | TUDpress | |
| dc.relation.ispartof | Workshop Gemeinschaften in Neuen Medien (GeNeMe) 2021 | |
| dc.subject | Wissensmanagement | |
| dc.subject | Transformation | |
| dc.subject | Wissensgemeinschaften | |
| dc.subject | Rekonstruktion historischer Objekte | |
| dc.title | Processing History | en |
| dc.type | Text/Conference Paper | |
| gi.citation.publisherPlace | Dresden |
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