FashionReGen: LLM-Empowered Fashion Report Generation
<|reference_start|>FashionReGen: LLM-Empowered Fashion Report Generation: Fashion analysis refers to the process of examining and evaluating trends, styles, and elements within the fashion industry to understand and interpret its current state, generating fashion reports. It is traditionally performed by fashion professionals based on their expertise and experience, which requires high labour cost and may also produce biased results for relying heavily on a small group of people. In this paper, to tackle the Fashion Report Generation (FashionReGen) task, we propose an intelligent Fashion Analyzing and Reporting system based the advanced Large Language Models (LLMs), debbed as GPT-FAR. Specifically, it tries to deliver FashionReGen based on effective catwalk analysis, which is equipped with several key procedures, namely, catwalk understanding, collective organization and analysis, and report generation. By posing and exploring such an open-ended, complex and domain-specific task of FashionReGen, it is able to test the general capability of LLMs in fashion domain. It also inspires the explorations of more high-level tasks with industrial significance in other domains. Video illustration and more materials of GPT-FAR can be found in https://github.com/CompFashion/FashionReGen.<|reference_end|>
@article{ding2024fashionregen:,
    title={FashionReGen: LLM-Empowered Fashion Report Generation},
    author={Yujuan Ding, Yunshan Ma, Wenqi Fan, Yige Yao, Tat-Seng Chua, Qing Li},
    journal={arXiv preprint arXiv:2403.06660},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06660},
    primaryClass={cs.MM cs.AI cs.MA}
}