한국정보처리학회:학술대회논문집 (Annual Conference of KIPS)

한국정보처리학회 (Korea Information Processing Society)
권호 표지

패션 추천에서 멀티모달 파운데이션 모델에 관한 연구

A Study of MultiModal Foundation Model in Fashion Recommendation

  • 데레 로시다트 올루와부콜라 (전남대학교 인공지능융합학과) ;
  • 김경백 (전남대학교 인공지능융합학과)
  • Dere Roshidat Oluwabukola (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Kyungbeak Kim (Dept. of Artificial Intelligence Convergence, Chonnam National University)
  • 발행 : 2024.10.31
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초록

Influenced by societal trends, cultural standards, and individual personalitiees, fashion is a potent means of self-expression. Many industries have benefited from the advancement of Artificial Intelligence(AI), with the fashion industry emerging as one of the most notable. AI has assisted the fashion industry in a number of areas, including product design and marketing. Online buying has proliferated as the fashion business has expanded into a multibillion-dollar industry, offering customers easy, stress-free shopping experiences. By advising customers on what to buy there could be potential increase in the sales of such and other products. The goal of this study is to investigate qualitatively mutimodal foundation models for fashion critics and advice. In this paper, we adapted a Gemini 1.5 flash on our dataset for compatibility prediction and complementary commentary on clothing. Qualitatively, the model provided very indepth review with varying images while also criticing fashion combination that are not compabible. The study alludes to the robotuness of mutimodal models with reommendation on quantitative evaluation in future studies.

키워드

과제정보

This work was supported by Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT)(IITP-2024-RS-2022-00156287, 50%). This work was supported by Institute of Information & Communications Technology Planning & Evaluation(IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development(IITP-2023-RS-2023-00256629, 50%) grant funded by the Korea government (MSIT).

참고문헌

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