Multi-Modal Fashion Product Retrieval

Antonio Rubio Romano, LongLong Yu, Edgar Simo-Serra, Francesc Moreno-Noguer


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 large-scale e-commerce dataset.
Anthology ID:
W17-2007
Volume:
Proceedings of the Sixth Workshop on Vision and Language
Month:
April
Year:
2017
Address:
Valencia, Spain
Venues:
VL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
43–45
Language:
URL:
https://www.aclweb.org/anthology/W17-2007
DOI:
10.18653/v1/W17-2007
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2007.pdf