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DC poleHodnotaJazyk
dc.contributor.authorGavrilova, Marina
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-24T19:00:32Z
dc.date.available2024-07-24T19:00:32Z
dc.date.issued2024
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 1-2.en
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57371
dc.format2 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencycs_CZ
dc.subjectgenerativní AIcs
dc.subjectpočítačová grafikacs
dc.subjecthluboké učenícs
dc.subjectbiometriecs
dc.subjectdigitální člověkcs
dc.subjectautenticitacs
dc.subjecttendencecs
dc.subjectdůvěracs
dc.titleA Synergy of Computer Graphics and Generative AI: Advancements and Challengesen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedA traditional computer graphics domain has received an unprecedented boost from the newest developments in generative Artificial Intelligence (GenAI). It affects all areas: from image generation, to face recognition, to object detection, to aerial surveillance, to autonomous car vision systems. The newest deep learning architectures make it possible to generate new images from texts, to apply styles to portraits, to de-identify facial images, and to recognize human and objects in videos. This keynote will delve into some of the most exciting applications in medical AI diagnostics, human face recognition and aesthetics domains, while making a strong case for resulting image authenticity, bias mitigation, and trusten
dc.subject.translatedgenerative AIen
dc.subject.translatedcomputer graphicsen
dc.subject.translateddeep learningen
dc.subject.translatedbiometricsen
dc.subject.translateddigital humanen
dc.subject.translatedauthenticityen
dc.subject.translatedbiasen
dc.subject.translatedtrusten
dc.identifier.doihttps://doi.org/10.24132/10.24132/CSRN.3401.1
dc.type.statusPeer revieweden
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