Multi-Label Classification for Fashion Data: Zero-Shot Classifiers via Few-Shot Learning on Large Language Models

Dongming Jiang, Abhishek Shah, Stanley Yeung, Jessica Zhu, Karan Singh, George Goldenberg

2024

Abstract

Multi-Label classification is essential in the fashion industry due to the complexity of fashion items, which often have multiple attributes such as style, material, and occasion. Traditional machine-learning approaches face challenges like data imbalance, high dimensionality, and the constant emergence of new styles and labels. To address these issues, we propose a novel approach that leverages Large Language Models (LLMs) by integrating few-shot and zero-shot learning. Our methodology utilizes LLMs to perform few-shot learning on a small, labeled dataset, generating precise descriptions of new fashion classes. These descriptions guide the zero-shot learning process, allowing for the classification of new items and categories with minimal labeled data. We demonstrate this approach using OpenAI’s GPT-4, a state-of-the-art LLM. Experiments on a dataset from CaaStle Inc., containing 2,480 unique styles with multiple labels, show significant improvements in classification performance. Few-shot learning enhances the quality of zero-shot classifiers, leading to superior results. GPT-4’s multi-modal capabilities further improve the system’s effectiveness. Our approach provides a scalable, flexible, and accurate solution for fashion classification, adapting to dynamic trends with minimal data requirements, thereby improving operational efficiency and customer experience. Additionally, this method is highly generalizable and can be applied beyond the fashion industry.

Paper Citation

in Harvard Style

Jiang D., Shah A., Yeung S., Zhu J., Singh K. and Goldenberg G. (2024). Multi-Label Classification for Fashion Data: Zero-Shot Classifiers via Few-Shot Learning on Large Language Models. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 250-257. DOI: 10.5220/0012899100003838

in Bibtex Style

@conference{kdir24,
author={Dongming Jiang and Abhishek Shah and Stanley Yeung and Jessica Zhu and Karan Singh and George Goldenberg},
title={Multi-Label Classification for Fashion Data: Zero-Shot Classifiers via Few-Shot Learning on Large Language Models},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={250-257},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012899100003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Multi-Label Classification for Fashion Data: Zero-Shot Classifiers via Few-Shot Learning on Large Language Models
SN - 978-989-758-716-0
AU - Jiang D.
AU - Shah A.
AU - Yeung S.
AU - Zhu J.
AU - Singh K.
AU - Goldenberg G.
PY - 2024
SP - 250
EP - 257
DO - 10.5220/0012899100003838
PB - SciTePress