33

dc.contributor.advisor33
dc.contributor.authorSaavedra, Jose M.
dc.contributor.authorMorales, Javier
dc.contributor.authorMurrugarra-Llerena, Nils
dc.coverageDOI: 10.1007/s00521-022-07978-9
dc.date2023
dc.date.accessioned05-01-2026 18:14
dc.date.available05-01-2026 18:14
dc.date.issued33
dc.description.abstractSketch-based image retrieval is demanding interest in the computer vision community due to its relevance in the visual perception system and its potential application in a wide diversity of industries. In the literature, we observe significant advances when the models are evaluated in public datasets. However, when assessed in real environments, the performance drops drastically. The big problem is that the SOTA SBIR models follow a supervised regimen, strongly depending on a considerable amount of labeled sketch-photo pairs, which is unfeasible in real contexts. Therefore, we propose SBIR-BYOL, an extension of the well-known BYOL, to work in a bimodal scenario for sketch-based image retrieval. To this end, we also propose a two-stage self-supervised training methodology, exploiting existing sketch-photo pairs and contour-photo pairs generated from photographs of a target catalog. We demonstrate the benefits of our model for the eCommerce environments, where searching is a critical component. Here, our self-supervised SBIR model shows an increase of over 60 % of mAP.
dc.identifierhttps://investigadores.uandes.cl/en/publications/00438573-0ff6-4308-b75f-b2b05ccde998
dc.identifier.citation33
dc.identifier.uri33
dc.languageeng
dc.language.iso33
dc.publisher33
dc.relation33
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourcevol.35 (2023) nr.7 p.5395-5408
dc.subjectDeep-learning
dc.subjectRepresentation learning
dc.subjectSelf-supervision
dc.subjectSketch-based image retrieval
dc.title33
dc.titleSBIR-BYOL: a self-supervised sketch-based image retrieval modeleng
dc.title33spa
dc.title33und
dc.type33
dc.typeArticleeng
dc.typeArtículospa
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