An Empirical Analysis of GPT-4V's Performance on Fashion Aesthetic Evaluation
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arXiv
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| Format: | Preprint |
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2024
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| author | Hirakawa, Yuki Wada, Takashi Morishita, Kazuya Shimizu, Ryotaro Furusawa, Takuya Kham, Sai Htaung Saito, Yuki |
| author_facet | Hirakawa, Yuki Wada, Takashi Morishita, Kazuya Shimizu, Ryotaro Furusawa, Takuya Kham, Sai Htaung Saito, Yuki |
| contents | Fashion aesthetic evaluation is the task of estimating how well the outfits worn by individuals in images suit them. In this work, we examine the zero-shot performance of GPT-4V on this task for the first time. We show that its predictions align fairly well with human judgments on our datasets, and also find that it struggles with ranking outfits in similar colors. The code is available at https://github.com/st-tech/gpt4v-fashion-aesthetic-evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23730 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Empirical Analysis of GPT-4V's Performance on Fashion Aesthetic Evaluation Hirakawa, Yuki Wada, Takashi Morishita, Kazuya Shimizu, Ryotaro Furusawa, Takuya Kham, Sai Htaung Saito, Yuki Computer Vision and Pattern Recognition Fashion aesthetic evaluation is the task of estimating how well the outfits worn by individuals in images suit them. In this work, we examine the zero-shot performance of GPT-4V on this task for the first time. We show that its predictions align fairly well with human judgments on our datasets, and also find that it struggles with ranking outfits in similar colors. The code is available at https://github.com/st-tech/gpt4v-fashion-aesthetic-evaluation. |
| title | An Empirical Analysis of GPT-4V's Performance on Fashion Aesthetic Evaluation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.23730 |