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dc.contributor.authorHrúz, Marek
dc.contributor.authorGruber, Ivan
dc.contributor.authorKanis, Jakub
dc.contributor.authorBoháček, Matyáš
dc.contributor.authorHlaváč, Miroslav
dc.contributor.authorKrňoul, Zdeněk
dc.date.accessioned2023-03-06T11:00:26Z-
dc.date.available2023-03-06T11:00:26Z-
dc.date.issued2022
dc.identifier.citationHRÚZ, M. GRUBER, I. KANIS, J. BOHÁČEK, M. HLAVÁČ, M. KRŇOUL, Z. One Model is Not Enough: Ensembles for Isolated Sign Language Recognition. SENSORS, 2022, roč. 22, č. 13, s. nestránkováno. ISSN: 1424-8220cs
dc.identifier.issn1424-8220
dc.identifier.uri2-s2.0-85133217387
dc.identifier.urihttp://hdl.handle.net/11025/51652
dc.format17 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherMDPIen
dc.relation.ispartofseriesSENSORSen
dc.rights© authorsen
dc.titleOne Model is Not Enough: Ensembles for Isolated Sign Language Recognitionen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedIn this paper, we dive into sign language recognition, focusing on the recognition of isolated signs. The task is defined as a classification problem, where a sequence of frames (i.e., images) is recognized as one of the given sign language glosses. We analyze two appearance-based approaches, I3D and TimeSformer, and one pose-based approach, SPOTER. The appearance-based approaches are trained on a few different data modalities, whereas the performance of SPOTER is evaluated on different types of preprocessing. All the methods are tested on two publicly available datasets: AUTSL and WLASL300. We experiment with ensemble techniques to achieve new state-of-the-art results of 73.84% accuracy on the WLASL300 dataset by using the CMA-ES optimization method to find the best ensemble weight parameters. Furthermore, we present an ensembling technique based on the Transformer model, which we call Neural Ensembler.en
dc.subject.translatedsign language recognitionen
dc.subject.translatedCNNen
dc.subject.translatedTransformeren
dc.subject.translatedensembleen
dc.identifier.doi10.3390/s22135043
dc.type.statusPeer-revieweden
dc.identifier.document-number824167200001
dc.identifier.obd43937108
dc.project.IDTN01000024/Národní centrum kompetence - Kybernetika a umělá inteligencecs
dc.project.ID90042/Velká výzkumná infrastruktura povinnost (J) - CESNET IIcs
dc.project.IDEF15_003/0000466/Umělá inteligence a uvažovánícs
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Články / Articles (KKY)
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