SBIR-BYOL: a self-supervised sketch-based image retrieval model

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dc.coverageDOI: 10.1007/s00521-022-07978-9
dc.creatorSaavedra, Jose M.
dc.creatorMorales, Javier
dc.creatorMurrugarra-Llerena, Nils
dc.date2023
dc.date.accessioned2025-11-18T19:49:48Z
dc.date.available2025-11-18T19:49:48Z
dc.description<p>Sketch-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.</p>eng
dc.identifierhttps://investigadores.uandes.cl/en/publications/00438573-0ff6-4308-b75f-b2b05ccde998
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/56282
dc.languageeng
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.titleSBIR-BYOL: a self-supervised sketch-based image retrieval modeleng
dc.typeArticleeng
dc.typeArtículospa
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