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dc.contributor.authorGuo, Zhongliang
dc.contributor.authorArandelovic, Oggie
dc.contributor.authorReid, David
dc.contributor.authorLei, Yaxiong
dc.identifier.citationGuo , Z , Arandelovic , O , Reid , D & Lei , Y 2023 , ' A Siamese transformer network for zero-shot ancient coin classification ' , Journal of Imaging , vol. 9 , no. 6 , 107 .
dc.identifier.otherPURE: 286256413
dc.identifier.otherPURE UUID: 367138bb-8a9d-4fe8-8ff7-937e736be9ef
dc.identifier.otherORCID: /0000-0002-6025-3021/work/135851474
dc.description.abstractAncient numismatics, the study of ancient coins, has in recent years become an attractive domain for the application of computer vision and machine learning. Though rich in research problems, the predominant focus in this area to date has been on the task of attributing a coin from an image, that is of identifying its issue. This may be considered the cardinal problem in the field and it continues to challenge automatic methods. In the present paper, we address a number of limitations of previous work. Firstly, the existing methods approach the problem as a classification task. As such, they are unable to deal with classes with no or few exemplars (which would be most, given over 50,000 issues of Roman Imperial coins alone), and require retraining when exemplars of a new class become available. Hence, rather than seeking to learn a representation that distinguishes a particular class from all the others, herein we seek a representation that is overall best at distinguishing classes from one another, thus relinquishing the demand for exemplars of any specific class. This leads to our adoption of the paradigm of pairwise coin matching by issue, rather than the usual classification paradigm, and the specific solution we propose in the form of a Siamese neural network. Furthermore, while adopting deep learning, motivated by its successes in the field and its unchallenged superiority over classical computer vision approaches, we also seek to leverage the advantages that transformers have over the previously employed convolutional neural networks, and in particular their non-local attention mechanisms, which ought to be particularly useful in ancient coin analysis by associating semantically but not visually related distal elements of a coin’s design. Evaluated on a large data corpus of 14,820 images and 7605 issues, using transfer learning and only a small training set of 542 images of 24 issues, our Double Siamese ViT model is shown to surpass the state of the art by a large margin, achieving an overall accuracy of 81%. Moreover, our further investigation of the results shows that the majority of the method’s errors are unrelated to the intrinsic aspects of the algorithm itself, but are rather a consequence of unclean data, which is a problem that can be easily addressed in practice by simple pre-processing and quality checking.
dc.relation.ispartofJournal of Imagingen
dc.rightsCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://
dc.subjectSiamese neural networken
dc.subjectDeep learningen
dc.subjectComputer visionen
dc.subjectMachine learningen
dc.subjectLow-shot learningen
dc.subjectCJ Numismaticsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleA Siamese transformer network for zero-shot ancient coin classificationen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.description.statusPeer revieweden

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