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Found 6 papers, 1 papers with code
SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images
We propose to employ visual data to infer size and fit characteristics of fashion articles.
A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation.
A Hierarchical Bayesian Model for Size Recommendation in Fashion
We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion.
SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce
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.
FitGAN: Fit- and Shape-Realistic Generative Adversarial Networks for Fashion
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.
ALiSNet: Accurate and Lightweight Human Segmentation Network for Fashion E-Commerce
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.
