Título:
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Multi-modal joint embedding for fashion product retrieval
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Autor/a:
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Rubio Romano, Antonio; Yu, Longlong; Simó Serra, Edgar; Moreno-Noguer, Francesc
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Otros autores:
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Institut de Robòtica i Informàtica Industrial; Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI |
Abstract:
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Abstract:
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Finding a product in the fashion world can be a daunting task. Everyday, e-commerce sites are updating with thousands of images and their associated metadata (textual information), deepening the problem, akin to finding a needle in a haystack. In this paper, we leverage both the images and textual meta-data and propose a joint multi-modal embedding that maps both the text and images into a common latent space. Distances in the latent space correspond to similarity between products, allowing us to effectively perform retrieval in this latent space, which is both efficient and accurate. We train this embedding using large-scale real world e-commerce data by both minimizing the similarity between related products and using auxiliary classification networks to that encourage the embedding to have semantic meaning. We compare against existing approaches and show significant improvements in retrieval tasks on a large-scale e-commerce dataset. We also provide an analysis of the different metadata. |
Abstract:
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Peer Reviewed |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Àrees temàtiques de la UPC::Informàtica::Automàtica i control -active vision -learning (artificial intelligence) -image-text embedding -deep learning -fashion -Classificació INSPEC::Cybernetics::Artificial intelligence |
Derechos:
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Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Tipo de documento:
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Artículo - Versión presentada Objeto de conferencia |
Editor:
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Institute of Electrical and Electronics Engineers (IEEE)
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