Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/91487
Título: | A regression deep learning approach for fashion compatibility |
Autor(es): | Silva, Luís Gomes, Ivan Araújo, C. Mendes Cepeda, Tiago Oliveira, Francisco Oliveira, João |
Palavras-chave: | Visual Search Deep learning Outfit BiLSTM CNN Compatibility learning Transformer Similarity learning |
Data: | 2024 |
Editora: | SCITEPRESS – Science and Technology Publications |
Citação: | 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; ISSN 2184-4992, SciTePress, pages 141-148. DOI: 10.5220/0012682300003690 |
Resumo(s): | 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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/91487 |
ISBN: | 978-989-758-692-7 |
DOI: | 10.5220/0012682300003690 |
Versão da editora: | https://www.scitepress.org/PublicationsDetail.aspx?ID=bc5t68lUGbk=&t=1 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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ICEIS_2024_160_CR.pdf | 518,26 kB | Adobe PDF | Ver/Abrir |