A regression deep learning approach for fashion compatibility

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.