Exploring Fit and Shape for Predicting Garment Size Issues in Fashion with Multi-task Learning

Online shoppers face many difficulties finding the right garment size that fits them. There has been a large body of work that try to tackle this problem from a customer perspective. Most often the approaches rely solely on purchases and returns to offer a personalized size-recommendation. In this work we investigate the potential effect of additional fashion characteristics such as shape and fit attributes of an article to better predict a potential size issue even before a purchase has been made. We frame the problem as a Multi-Task-Learning (MTL) problem and extend prior work to predict size issues from garments visual information. In the experiments, we explore different MTL architectures and a variety of datasets to further harness the power of these models. To the best of our knowledge, the proposed approach is the first to combine size, fit and shape attributes of fashion articles to address the challenge of size issue prediction in online shopping. Our findings indicate that there is a positive effect of additional attributes and that we can improve the size-issue prediction in comparison to the state-of-the-art models.