SBIR Phase I: Scaling Up Open Innovation with Crowd Wisdom and Artificial Intelligence (AI) for Smarter and More Sustainable Fashion

SBIR 第一阶段：利用群体智慧和人工智能 (AI) 扩大开放创新，打造更智能、更可持续的时尚

• 批准号: 2223164
• 负责人: HAIYONG ZHANG
• 金额: $ 27.47万
• 依托单位: ILAMBDA, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-15 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223164&HistoricalAwards=false
• 关键词: SBIR; Phase; Scaling; Up; Open

This Small Business Innovation Research (SBIR) Phase I project will develop and leverage an innovative hybrid intelligence, i.e., a unique combination of Big Data and artificial intelligence (AI) technologies with the wisdom of crowds, to help connect and empower both independent designers and small-to-medium-sized retailers/fashion buyers (together with supply chain partners), to help bring the original, unique, trendy designs with great garment quality to fashion consumers. The project also aims to help the fashion industry to tackle some of its hardest, most critical, and most urgent challenges in overproduction and waste (resulting in environmental issues). The project will advance recommendation technology and fashion intelligence by developing novel deep learning-powered fashion recommendation models, and effectively combine and integrate human fashion experts’ input and deep learning predictions. These techniques will help match fashion retail buyers and design(er)s, with the consideration of uniqueness and exclusivity. The project will also help evaluate key aspects of the fashion designs, such as uniqueness and trendiness, and provide more accurate predictions on fashion demands and sales. The key technology innovations are two-fold. First, a novel self-supervised and deep learning-powered fashion recommendation engine will effectively utilize the heterogeneous fashion data (images, text, behaviors, and sales) to help accurately match fashion buyers and manufacturers with the (new) design(er)s under style compatibility and other requirements. Second, a hybrid intelligence engine will effectively combine and integrate fashion buyers' input (votes and orders) with deep learning models to help measure fashion uniqueness, trendiness, and sales forecasts, etc., of the new designs. The project can help both designers and retailers track the trends and the demands and stay ahead of the fashion curve.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这是小型企业创新研究（SBIR）I阶段项目，并利用创新的混合智能，具有人群智慧的大数据和人工智能（AI）技术（AI）技术，以帮助连接和赋予两者独立设计师的能力中型零售商/时尚购买者（以及供应链合作伙伴），以将最初的时尚设计具有出色的质量，以帮助时尚消费者。通过开发学习驱动的时尚推荐模型，最紧迫的挑战是淘汰技术的策略，使人类时尚专家的投入和深度学习预测有助于零售。时装设计（例如独特性和趋势），并为时尚需求和销售提供了更准确的预测。销售有效地将时尚购买者和制造商与（新的）Sunder Style兼容性和其他要求相匹配，这将有效地结合和整合时尚购买者的投入（投票和订单），以帮助衡量时尚独特性和销售对新设计的预测等。该项目都可以帮助设计师跟踪E趋势和需求，并保持领先于时尚曲线。该奖项反映了NSF'SFLY的任务使用Toundation的智力优点和更广泛的影响审查标准。