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
- Editors:
- Anya Belz, Erkut Erdem, Katerina Pastra, Krystian Mikolajczyk
- Venue:
- VL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 43–45
- Language:
- URL:
- https://aclanthology.org/W17-2007
- DOI:
- 10.18653/v1/W17-2007
- Cite (ACL):
- Antonio Rubio Romano, LongLong Yu, Edgar Simo-Serra, and Francesc Moreno-Noguer. 2017. Multi-Modal Fashion Product Retrieval. In Proceedings of the Sixth Workshop on Vision and Language, pages 43–45, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Modal Fashion Product Retrieval (Rubio Romano et al., VL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W17-2007.pdf