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Knowledge Enhanced Neural Fashion Trend Forecasting. In ICMR . ACM, 82–90
Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, and Tat-Seng Chua. 2020 · 2020
Earlier work this paper cites.
Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media
Yujuan Ding, Yunshan Ma, Lizi Liao, Wai Keung Wong, and Tat-Seng Chua. 2022 · 2022
Earlier work this paper cites.

Similar

Is GPT-4 a Good Data Analyst?. In EMNLP (Findings) . ACL, 9496–9514
Liying Cheng, Xingxuan Li, and Lidong Bing. 2023 · 2023
Cited alongside, same era.
Personalized fashion outfit generation with user coordination preference learning
Yujuan Ding, P. Y. Mok, Yunshan Ma, and Yi Bin. 2023 · 2023
Cited alongside, same era.

Then

The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)
Original
Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, and Lijuan Wang. 2023 · 2023
Later among the works it cites.

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2024

FashionReGen: LLM-Empowered Fashion Report Generation

Ding, Yujuan, Ma, Yunshan, Fan, Wenqi et al.

Understand

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.