no code implementations • 15 Apr 2023 • Amrollah Seifoddini, Koen Vernooij, Timon Künzle, Alessandro Canopoli, Malte Alf, Anna Volokitin, Reza Shirvany
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.
no code implementations • 23 Jun 2022 • Sonia Pecenakova, Nour Karessli, Reza Shirvany
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.
no code implementations • 7 Jun 2021 • Andrea Nestler, Nour Karessli, Karl Hajjar, Rodrigo Weffer, Reza Shirvany
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.
no code implementations • 2 Aug 2019 • Romain Guigourès, Yuen King Ho, Evgenii Koriagin, Abdul-Saboor Sheikh, Urs Bergmann, Reza Shirvany
We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion.
3 code implementations • 23 Jul 2019 • Abdul-Saboor Sheikh, Romain Guigoures, Evgenii Koriagin, Yuen King Ho, Reza Shirvany, Roland Vollgraf, Urs Bergmann
To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation.
no code implementations • 28 May 2019 • Nour Karessli, Romain Guigourès, Reza Shirvany
We propose to employ visual data to infer size and fit characteristics of fashion articles.