Toward Effective Fashion Item Compatibility Modelling

The rise in online fashion retail has led to increased research in fashion compatibility modeling and item retrieval. These technologies help users find fashion items based on text descriptions or reference images, focusing on how well items go together. However, retrieving complementary items is challenging due to the need for precise compatibility models. We propose the Compatibility-to-Retrieval Model (C2RM), which aims to improve fashion image retrieval using image-to-image translation. First, a Conditional Generative Adversarial Network generates target items from query items. Next, these generated samples are fed into C2RM, enhancing compatibility modeling and retrieval accuracy exploiting the first and second step. Evaluations on two datasets show C2RM’s superior performance over current baselines. © 2022 Copyright for this paper by its authors.