%I ACM
%K Human-AI interaction, AI for Social Good, Sustainability, Machine Learning, Multimodal Large Language Models, Agents
%L discovery10196395
%C San Jose, Costa Rica
%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.
%O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
%T Design Digital Multisensory Textile Experiences
%A Shu Zhong
%B Proceedings of the 26th ACM International Conference on Multimodal Interaction
%D 2024