A Regression Deep Learning Approach for Fashion Compatibility

Luís Silva, Ivan Gomes, C. Araújo, Tiago Cepeda, Francisco Oliveira, João Oliveira

2024

Abstract

In the ever-evolving world of fashion, building the perfect outfit can be a challenge. We propose a fashion recommendation system, which we call Visual Search, that uses computer vision and deep learning to ensure that it has a co-ordinated set of fashion recommendations. It looks at photos of incomplete outfits, recognizes existing items, and suggests the most compatible missing piece. At the heart of our system lies a compatibility model made of a Convolutional Neural Network and bidirectional Long Short Term Memory to generate a complementary missing piece. To complete the recommendation process, we incorporated a similarity model, based on Vision Transformer. This model meticulously compares the generated image to the catalog items, selecting the one that most closely matches the generated image in terms of visual features.

Paper Citation

in Harvard Style

Silva L., Gomes I., Araújo C., Cepeda T., Oliveira F. and Oliveira J. (2024). A Regression Deep Learning Approach for Fashion Compatibility. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 141-148. DOI: 10.5220/0012682300003690

in Bibtex Style

@conference{iceis24,
author={Luís Silva and Ivan Gomes and C. Araújo and Tiago Cepeda and Francisco Oliveira and João Oliveira},
title={A Regression Deep Learning Approach for Fashion Compatibility},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={141-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012682300003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A Regression Deep Learning Approach for Fashion Compatibility
SN - 978-989-758-692-7
AU - Silva L.
AU - Gomes I.
AU - Araújo C.
AU - Cepeda T.
AU - Oliveira F.
AU - Oliveira J.
PY - 2024
SP - 141
EP - 148
DO - 10.5220/0012682300003690
PB - SciTePress