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. And a third proposes a system that captures clothing’s affinity with different body shapes.
Ecommerce businesses like Facebook Marketplace lean on AI to automate a host of behind-the-scenes tasks, from learning preferences and body types to understanding the factors that might influence purchase decisions. McKinsey estimates that Amazon, which recently deployed AI to handle incoming shopper inquiries, generates 35% of all sales from its product recommendation engine. Beyond ranking, AI from startups like ModiFace, Vue.Ai, Edited, Syte, and Adverity enable customers to try on shades of lipstick virtually, see model images in every size, and spot trends and sales over time.Discovering fashion style influences
As an engineer at Facebook AI Research notes in one of the papers, the clothes people wear are a function of factors like comfort, taste, and occasion but also wider and subtler influences like changing social norms, art, politics, celebrities, style icons, the weather, and the “mood” of a city in which someone lives. For this reason, quantitatively pinpointing the influences in fashion remains an intractable challenge.VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.
The Facebook researcher, then, proposes discovering influence patterns in large photo galleries and leveraging those patterns to forecast style trends. “We contend that images are exactly the right data to answer such questions,” Kristen Grauman and Ziad Al-Halah, a coauthor at the University of Texas at Austin, wrote in the paper. “Unlike vendors’ purchase data, other non-visual metadata, or hype from haute couture designers, everyday photos of what people are wearing in their daily life provide an unfiltered glimpse of current clothing styles ‘on the ground.’”
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