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Título

Multi-modal joint embedding for fashion product retrieval

AutorRubio Romano, Antonio; Yu, LongLong; Simo-Serra, Edgar; Moreno-Noguer, Francesc
Palabras claveNeural networks
Multi-modal embedding
Retrieva
Fecha de publicación2017
EditorInstitute of Electrical and Electronics Engineers
CitaciónIEEE International Conference on Image Processing: 400-404 (2017)
ResumenFinding 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.
DescripciónTrabajo presentado a la IEEE International Conference on Image Processing (ICIP), celebrada en Beijing (China) del 17 al 20 de septiembre de 2017.
Versión del editorhttps://doi.org/10.1109/ICIP.2017.8296311
URIhttp://hdl.handle.net/10261/167051
Identificadoresdoi: 10.1109/ICIP.2017.8296311
isbn: 978-1-5090-2176-5
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