OutCLIP, A New Multi-Outfit CLIP Based Triplet Network

Abstract

Choosing a proper outfit is one of the problems we deal with every day. Today, people tend to use online websites for shopping, and the COVID-19 situation forced this condition more than before. In this research, we proposed a new architecture for multi-fashion item retrieval from a website database. We deployed a CLIP transformer model instead of convolutional neural networks in a triplet network. We also added a long short-term memory network (LSTM) to automatically extract and code the image features to generate descriptive text for each input image. Our OutCLIP model succeeded in doing its task with 83% precision and 85% recall accuracy in multi-item retrieval. This model can be trained and used in fashion retrieval problems and improve the former proposed models. Considering the descriptive text and the image together gives the model a better understanding of the concept and improves its generalization.