Towards an Interoperable Data Protocol Aimed at Linking the Fashion Industry with AI Companies
Mohammed Al-Rawi, Joeran Beel

TL;DR
This paper proposes a standardized data protocol called DDOIF to facilitate seamless data exchange and interoperability between the fashion industry and AI companies, addressing current data structure and terminology inconsistencies.
Contribution
The paper introduces the DDOIF protocol, a comprehensive framework for organizing and exchanging fashion data, including an API and a detailed dictionary of fashion terminology.
Findings
Developed the DDOIF protocol with extensive fashion attributes
Built a dictionary of over 1000 fashion class and subclass names
Made DDOIF publicly available for collaboration and improvement
Abstract
The fashion industry is looking forward to use artificial intelligence technologies to enhance their processes, services, and applications. Although the amount of fashion data currently in use is increasing, there is a large gap in data exchange between the fashion industry and the related AI companies, not to mention the different structure used for each fashion dataset. As a result, AI companies are relying on manually annotated fashion data to build different applications. Furthermore, as of this writing, the terminology, vocabulary and methods of data representation used to denote fashion items are still ambiguous and confusing. Hence, it is clear that the fashion industry and AI companies will benefit from a protocol that allows them to exchange and organise fashion information in a unified way. To achieve this goal we aim (1) to define a protocol called DDOIF that will allow…
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Taxonomy
Topics3D Shape Modeling and Analysis · Fashion and Cultural Textiles · Generative Adversarial Networks and Image Synthesis
