Main product detection with graph networks for fashion
Yazici, Vacit Oguz (Centre de Visió per Computador (Bellaterra, Catalunya))
Yu, L. (Wide-Eyes Technologies)
Ramisa, A. (Amazon Inc.)
Herranz, Luis (Centre de Visió per Computador (Bellaterra, Catalunya))
van de Weijer, Joost (Centre de Visió per Computador (Bellaterra, Catalunya))

Date:	2022
Abstract:	Computer vision has established a foothold in the online fashion retail industry. Main product detection is a crucial step of vision-based fashion product feed parsing pipelines, focused on identifying the bounding boxes that contain the product being sold in the gallery of images of the product page. The current state-of-the-art approach does not leverage the relations between regions in the image, and treats images of the same product independently, therefore not fully exploiting visual and product contextual information. In this paper, we propose a model that incorporates Graph Convolutional Networks (GCN) that jointly represent all detected bounding boxes in the gallery as nodes. We show that the proposed method is better than the state-of-the-art, especially, when we consider the scenario where title-input is missing at inference time and for cross-dataset evaluation, our method outperforms previous approaches by a large margin.
Grants:	Agencia Estatal de Investigación PID2019-104174GB-I00 Agencia Estatal de Investigación RTI2018-102285-A-I00 Ministerio de Ciencia e Innovación RYC2019-027020-I
Note:	Altres ajuts: acords transformatius de la UAB
Note:	Altres ajuts: Industrial Doctorate Grant 2016 DI 039
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Main product detection ; Graph networks ; Fashion
Published in:	Multimedia Tools and Applications, (August 2022) , ISSN 1573-7721

DOI: 10.1007/s11042-022-13572-x

17 p, 5.6 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > GIES (Grup de Recerca en Estrès i Salut)
Articles > Research articles
Articles > Published articles