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.

这个小型企业创新研究(SBIR)第一阶段项目将开发和利用创新的混合智能，即大数据和人工智能(AI)技术与群体智慧的独特组合，帮助连接和授权独立设计师和中小型零售商/时尚买家(与供应链合作伙伴一起)，帮助将原创、独特、时尚的设计带给时尚消费者。该项目还旨在帮助时尚业解决生产过剩和浪费(导致环境问题)方面的一些最困难、最关键和最紧迫的挑战。该项目将通过开发新颖的深度学习时尚推荐模型来推进推荐技术和时尚智能，并有效地结合和整合人类时尚专家的输入和深度学习预测。这些技巧将有助于匹配时尚零售买家和设计(呃)S，考虑到独特性和排他性。该项目还将帮助评估时尚设计的关键方面，如独特性和时尚性，并对时尚需求和销售提供更准确的预测。关键技术创新是双重的。首先，一个新颖的自我监督和深度学习驱动的时尚推荐引擎将有效利用异质的时尚数据(图像、文本、行为和销售)，帮助时尚买家和制造商在风格兼容等要求下准确匹配时尚买家和制造商与(新)设计(呃)S。其次，混合智能引擎将有效地将时尚买家的输入(投票和订单)与深度学习模型结合起来，帮助衡量新设计的时尚独特性、时尚性和销售预测等。该项目可以帮助设计师和零售商跟踪趋势和需求，并保持在时尚曲线的领先地位。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。