SBIR Phase II: Innovative visual search and similarity for decor, apparel, and style

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力是开发视觉搜索，用于家具和家居装饰垂直领域的产品识别。 基于文本的搜索已经彻底改变了人们更快更有效地完成任务的能力，因为他们能够以有组织的、编译的和逻辑的方式找到他们想要的信息。视觉搜索提供了搜索功能的下一个层次的中断，允许用户通过使用图像更快速，更准确地找到信息。正在开发的基于深度学习的软件将使消费者能够快速找到他们感兴趣的产品，并共同购买相关产品。此外，用户将通过接触产品的设计师照片（灵感摄影）来更多地参与。通过帮助客户及时准确地找到他们正在寻找的内容，用户参与度和生产力将得到提高。此外，基于风格的相关建议将增加整体购买量。 增加消费通过增加零售商的应税收入和增加购买产生的销售税来刺激经济增长。这个小型企业创新研究第二阶段项目旨在开发一个视觉搜索引擎，通过将重点从基于文本的探索转向基于视觉搜索的探索，来颠覆零售和电子商务。该平台最初针对室内装饰和家具，在这些领域，深度学习技术经过训练，可以在各种条件下识别产品。在第二阶段，软件深度学习架构将被推广，以支持更广泛的产品，并允许客户对设计决策和选择进行更多的控制。面向客户端的REST API将允许零售商、设计师和媒体公司以编程方式访问平台的功能，并在深度学习技术之上构建自己的用户界面和应用程序。最后，建议开发一个白标签应用程序，可以为希望将这种视觉搜索功能分发给客户的个体零售商定制。 实现这些目标将在室内设计、服装搜索、真实的房地产搜索和产品查找等应用中创造最先进的视觉搜索性能。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to develop visual search for product recognition in the furniture and home décor vertical. Text-based searches have revolutionized the ability of people to complete tasks more quickly and efficiently as they are able to find the information they desire in an organized, compiled, and logical manner. Visual search provides the next level of disruption in search capabilities by allowing users to find information even more rapidly and accurately by using images. The deep learning-based software being developed will allow consumers to find products they are interested in, and co-purchase related products, quickly. Further, users will be more engaged through exposure to designer photographs of products (inspirational photography). By helping customers find exactly what they are looking for in a timely manner, user engagement and productivity will be increased. Further, related style-based recommendations will increase purchasing overall. Increased spending stimulates economic growth by increasing taxable revenue by retailers, and through increased sales taxes generated from the purchases. This Small Business Innovative Research Phase II project seeks to develop a visual search engine that is poised to disrupt retail and ecommerce by switching the focus from text-based to visual search-based exploration. The platform initially targets interior décor and furniture where deep learning techniques are trained to recognize products across a wide range of conditions. In Phase II, the software deep learning architectures will be generalized to enable a broader range of products, and to allow customers more control over design decisions and choices. A client-facing REST API will allow retailers, designers, and media companies to programmatically access functionality of the platform, and build their own user interfaces and apps on top of the deep learning technology. Lastly, it is proposed to develop a white-label app that can be customized for individual retailers who want to distribute this visual search capability to their customers. Achieving these objectives will create state-of-the-art performance in visual search for applications in interior design, apparel search, real estate search, and product look-up.