Zero-shot fashion products clustering on social image streams
Computer Vision methods have been proposed to solve the problem of matching photographs containing some products from users in social media to products in retail catalogues. This is challenging due to the quality of the photographies, difficulties in dealing with garments and their category taxonomy...
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ftupcatalunyair:oai:upcommons.upc.edu:2117/348727 2024-09-15T18:03:50+00:00 Zero-shot fashion products clustering on social image streams Poveda Pena, Jonatan Tous Liesa, Rubén Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions 2019 4 p. application/pdf http://hdl.handle.net/2117/348727 https://doi.org/10.1007/978-3-030-37599-7_63 eng eng Springer https://link.springer.com/chapter/10.1007/978-3-030-37599-7_63 info:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/ info:eu-repo/grantAgreement/AGAUR/V PRI/2014 SGR 1051 Poveda, J.; Tous, R. Zero-shot fashion products clustering on social image streams. A: International Conference on Machine Learning, Optimization, and Data Science. "Machine Learning, Optimization, and Data Science, 5th International Conference, LOD 2019: Siena, Italy, September 10-13, 2019: proceedings". Berlín: Springer, 2019, p. 755-758. ISBN 978-3-030-37599-7. DOI 10.1007/978-3-030-37599-7_63. 978-3-030-37599-7 http://hdl.handle.net/2117/348727 doi:10.1007/978-3-030-37599-7_63 Open Access Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Machine learning Computer vision Online social networks Deep metric learning Zero-shot learning Clustering Supervised learning Aprenentatge automàtic Visió per ordinador Xarxes socials en línia Conference report 2019 ftupcatalunyair https://doi.org/10.1007/978-3-030-37599-7_63 2024-07-25T11:10:00Z Computer Vision methods have been proposed to solve the problem of matching photographs containing some products from users in social media to products in retail catalogues. This is challenging due to the quality of the photographies, difficulties in dealing with garments and their category taxonomy. A N-Shot Learning approach is required as retail catalogues may contain hundreds of different products for which, in many cases, only one image is provided. This framework can be solved by means of Deep Metric Learning (DML) techniques, in which a metric to discriminate similar than dissimilar samples is learnt. The performance of different authors tackling this problem varies a lot but even if they perform reasonably well, the set of elements they need to return in order to include the exact product is large. As after the query there is a person curating the results, it is important to return the smallest set of elements possible, being ideally just to return only one: the related product. This paper proposes to solve the image-to-product image matching problem through a product retrieval system using DML and Zero-short Learning, focusing on garments, and applying some of the last advances on clustering techniques. This work is partially supported by the Spanish Ministry of Economy and Competi-tivity under contract TIN2015-65316-P and by the SGR programme (2014-SGR-1051and 2017-SGR-962) of the Catalan Government. Peer Reviewed Postprint (author's final draft) Conference Object DML Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge 755 758 |
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Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
op_collection_id |
ftupcatalunyair |
language |
English |
topic |
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Machine learning Computer vision Online social networks Deep metric learning Zero-shot learning Clustering Supervised learning Aprenentatge automàtic Visió per ordinador Xarxes socials en línia |
spellingShingle |
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Machine learning Computer vision Online social networks Deep metric learning Zero-shot learning Clustering Supervised learning Aprenentatge automàtic Visió per ordinador Xarxes socials en línia Poveda Pena, Jonatan Tous Liesa, Rubén Zero-shot fashion products clustering on social image streams |
topic_facet |
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Machine learning Computer vision Online social networks Deep metric learning Zero-shot learning Clustering Supervised learning Aprenentatge automàtic Visió per ordinador Xarxes socials en línia |
description |
Computer Vision methods have been proposed to solve the problem of matching photographs containing some products from users in social media to products in retail catalogues. This is challenging due to the quality of the photographies, difficulties in dealing with garments and their category taxonomy. A N-Shot Learning approach is required as retail catalogues may contain hundreds of different products for which, in many cases, only one image is provided. This framework can be solved by means of Deep Metric Learning (DML) techniques, in which a metric to discriminate similar than dissimilar samples is learnt. The performance of different authors tackling this problem varies a lot but even if they perform reasonably well, the set of elements they need to return in order to include the exact product is large. As after the query there is a person curating the results, it is important to return the smallest set of elements possible, being ideally just to return only one: the related product. This paper proposes to solve the image-to-product image matching problem through a product retrieval system using DML and Zero-short Learning, focusing on garments, and applying some of the last advances on clustering techniques. This work is partially supported by the Spanish Ministry of Economy and Competi-tivity under contract TIN2015-65316-P and by the SGR programme (2014-SGR-1051and 2017-SGR-962) of the Catalan Government. Peer Reviewed Postprint (author's final draft) |
author2 |
Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
format |
Conference Object |
author |
Poveda Pena, Jonatan Tous Liesa, Rubén |
author_facet |
Poveda Pena, Jonatan Tous Liesa, Rubén |
author_sort |
Poveda Pena, Jonatan |
title |
Zero-shot fashion products clustering on social image streams |
title_short |
Zero-shot fashion products clustering on social image streams |
title_full |
Zero-shot fashion products clustering on social image streams |
title_fullStr |
Zero-shot fashion products clustering on social image streams |
title_full_unstemmed |
Zero-shot fashion products clustering on social image streams |
title_sort |
zero-shot fashion products clustering on social image streams |
publisher |
Springer |
publishDate |
2019 |
url |
http://hdl.handle.net/2117/348727 https://doi.org/10.1007/978-3-030-37599-7_63 |
genre |
DML |
genre_facet |
DML |
op_relation |
https://link.springer.com/chapter/10.1007/978-3-030-37599-7_63 info:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/ info:eu-repo/grantAgreement/AGAUR/V PRI/2014 SGR 1051 Poveda, J.; Tous, R. Zero-shot fashion products clustering on social image streams. A: International Conference on Machine Learning, Optimization, and Data Science. "Machine Learning, Optimization, and Data Science, 5th International Conference, LOD 2019: Siena, Italy, September 10-13, 2019: proceedings". Berlín: Springer, 2019, p. 755-758. ISBN 978-3-030-37599-7. DOI 10.1007/978-3-030-37599-7_63. 978-3-030-37599-7 http://hdl.handle.net/2117/348727 doi:10.1007/978-3-030-37599-7_63 |
op_rights |
Open Access |
op_doi |
https://doi.org/10.1007/978-3-030-37599-7_63 |
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