We obtain our final model, ALiSNet, with a size of 4MB and 97. 6$\pm$1. 0$\%$ mIoU, compared to Apple Person Segmentation, which has an accuracy of 94. 4$\pm$5. 7$\%$ mIoU on our dataset.
Through experiments on real world data at scale, we demonstrate how our approach is capable of synthesizing visually realistic and diverse fits of fashion items and explore its ability to control fit and shape of images for thousands of online garments.
Size and fit related returns severely impact 1. the customers experience and their dissatisfaction with online shopping, 2. the environment through an increased carbon footprint, and 3. the profitability of online fashion platforms.
We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion.
To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation.