%0 Generic
%A Zhong, Shu
%C San Jose, Costa Rica
%D 2024
%F discovery:10196395
%I ACM
%K Human-AI interaction, AI for Social Good, Sustainability, Machine Learning, Multimodal Large Language Models, Agents
%T Design Digital Multisensory Textile Experiences
%U https://discovery.ucl.ac.uk/id/eprint/10196395/
%X The rise of Machine Learning (ML) is gradually digitalizing and  reshaping the fashion industry, which is under pressure to achieve  Net Zero. However, the integration of ML/AI for sustainable and  circular practices remains limited due to a lack of domain-specific  knowledge and data. My doctoral research aims to bridge this gap  by designing digital multisensory textile experiences that enhance  the understanding of the textile domain for both AI systems and  humans. To this end, I develop TextileNet, the first fashion dataset  using textile taxonomies for textile materials identification and classification via computer vision, and TextileBot, a domain-specific  conversational agent. TextileBot integrates textile taxonomies with  large language models (LLMs) to engage consumers in sustainable  practices. Additionally, my research explores how multisensory  experiences can improve user understanding and how AI perceives  textiles. The overarching goal is to embed human expertise into machines, design immersive multisensory experiences, and facilitate  natural human-AI interactions that promote sustainable practices.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.