Publication
Multi-modal fashion product retrieval
Conference Article
Conference
Workshop on Vision and Language (VL)
Edition
6th
Pages
43-45
Doc link
http://www.aclweb.org/anthology/W17-2007
File
Authors
Abstract
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. In this paper, we leverage both the images and textual metadata 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. We compare against existing approaches and show significant improvements in retrieval tasks on a largescale e-commerce dataset.
Categories
image classification, learning (artificial intelligence).
Author keywords
image-text embedding; deep learning; fashion
Scientific reference
A. Rubio, L. Yu, E. Simo-Serra and F. Moreno-Noguer. Multi-modal fashion product retrieval, 6th Workshop on Vision and Language, 2017, Valencia, pp. 43-45.
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