Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/25738
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dc.contributor.authorIslam, T-
dc.contributor.authorMiron, A-
dc.contributor.authorLiu, X-
dc.contributor.authorLi, Y-
dc.date.accessioned2023-01-06T09:50:05Z-
dc.date.available2023-01-06T09:50:05Z-
dc.date.issued2022-12-12-
dc.identifierORCID iDs: Alina Miron https://orcid.org/0000-0002-0068-4495; Xiaohui Liu https://orcid.org/0000-0003-1589-1267; Yongmin Li https://orcid.org/0000-0003-1668-2440.-
dc.identifier.citationIslam, T. et al. (2022) 'SVTON: Simplified Virtual Try-On', 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), Bahamas, 12-14 December, pp. 1 - 6.en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/25738-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCopyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.rights.urihttps://www.ieee.org/publications/rights/rights-policies.html-
dc.source2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)-
dc.source2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)-
dc.subjectVirtual Try-on (VTON)en_US
dc.subjectgenerative adversarial network (GAN)en_US
dc.subjectU-Neten_US
dc.subjectsegmentationen_US
dc.subjectAffine Transformen_US
dc.titleSVTON: Simplified Virtual Try-Onen_US
dc.typeConference Paperen_US
pubs.finish-date2022-12-14-
pubs.finish-date2022-12-14-
pubs.publication-statusIn preparation-
pubs.start-date2022-12-12-
pubs.start-date2022-12-12-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
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