Odd Concepts, a Seoul-based AI-based fashion tech company, has introduced StyleCrush! And with StyleCrush comes an AI style discovery platform that allows customers to get style inspiration and purchase the product online. The platform allows customers to browse for different styles based on their tastes in a digital album, with AI then helping the shoppers to search for such products online and buy them. The customers can easily upload the style image they like or even paste the link in the platform. The AI system then searches for similar products online and gives customers the sources where they can buy the product easily.

In May, the same week Facebook announced Shops, a way for businesses to set up online stores for customers across Facebook, WhatsApp, Messenger, and Instagram, the tech giant detailed the AI and machine learning systems behind its ecommerce experiences. Facebook said its goal is to one day develop an assistant that can serve up product recommendations on the fly, and that can learn preferences by analyzing images of what's in a person's wardrobe while allowing the person to try new items on self-replicas and sell apparel that others can preview. A flurry of Facebook-authored papers accepted to the Conference on Computer Vision and Pattern Recognition (CVPR) 2020 suggest the company is on its way to developing the components of this assistant. One paper describes an algorithm that uncovers and quantifies fashion influences from images taken around the world. Another demonstrates an AI model that generates 3D models of people from single images.

Outfittery is an online personalized styling service targeted at men. We have hundreds of stylists who create thousands of bespoke outfits for our customers every day. A critical challenge faced by our stylists when creating these outfits is selecting an appropriate item of clothing that makes sense in the context of the outfit being created, otherwise known as style fit. Another significant challenge is knowing if the item is relevant to the customer based on their tastes, physical attributes and price sensitivity. At Outfittery we leverage machine learning extensively and combine it with human domain expertise to tackle these challenges. We do this by surfacing relevant items of clothing during the outfit building process based on what our stylist is doing and what the preferences of our customer are. In this paper we describe one way in which we help our stylists to tackle style fit for a particular item of clothing and its relevance to an outfit. A thorough qualitative and quantitative evaluation highlights the method's ability to recommend fashion items by style fit.

When creating an outfit, style is a criterion in selecting each fashion item. This means that style can be regarded as a feature of the overall outfit. However, in various previous studies on outfit generation, there have been few methods focusing on global information obtained from an outfit. To address this deficiency, we have incorporated an unsupervised style extraction module into a model to learn outfits. Using the style information of an outfit as a whole, the proposed model succeeded in generating outfits more flexibly without requiring additional information. Moreover, the style information extracted by the proposed model is easy to interpret. The proposed model was evaluated on two human-generated outfit datasets. In a fashion item prediction task (missing prediction task), the proposed model outperformed a baseline method. In a style extraction task, the proposed model extracted some easily distinguishable styles. In an outfit generation task, the proposed model generated an outfit while controlling its styles. This capability allows us to generate fashionable outfits according to various preferences.

In this paper, we aim to better understand the clothing fashion styles. There remain two challenges for us: 1) how to quantitatively describe the fashion styles of various clothing, 2) how to model the subtle relationship between visual features and fashion styles, especially considering the clothing collocations. Using the words that people usually use to describe clothing fashion styles on shopping websites, we build a Fashion Semantic Space (FSS) based on Kobayashi's aesthetics theory to describe clothing fashion styles quantitatively and universally. Then we propose a novel fashion-oriented multimodal deep learning based model, Bimodal Correlative Deep Autoencoder (BCDA) , to capture the internal correlation in clothing collocations. Employing the benchmark dataset we build with 32133 full-body fashion show images, we use BCDA to map the visual features to the FSS. The experiment results indicate that our model outperforms (+13% in terms of MSE) several alternative baselines, confirming that our model can better understand the clothing fashion styles. To further demonstrate the advantages of our model, we conduct some interesting case studies, including fashion trends analyses of brands, clothing collocation recommendation, etc.

Style classification (e.g., architectural, music, fashion) attracts an increasing attention in both research and industrial fields. Most existing works focused on low-level visual features composition for style representation. However, little effort has been devoted to automatic mid-level or high-level style features learning by reorganizing low-level descriptors. Moreover, styles are usually spread out and not easy to differentiate from one to another. In this paper, we call these less representative images as weak style images. To address these issues, we propose a consensus style centralizing auto-encoder (CSCAE) to extract robust style features to facilitate weak style classification. CSCAE is the ensemble of several style centralizing auto-encoders (SCAEs) with consensus constraint. Each SCAE centralizes each feature of certain category in a progressive way. We apply our method in fashion style classification and manga style classification as two example applications. In addition, we collect a new dataset, Online Shopping, for fashion style classification evaluation, which will be publicly available for vision based fashion style research. Experiments demonstrate the effectiveness of SCAE and CSCAE on both public and newly collected datasets when compared with the most recent state-of-the-art works.