An Empirical Analysis of GPT-4V's Performance on Fashion Aesthetic Evaluation
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 31 Oct 2024.
Builds on
8 of 15 references.
References · 8
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2023Large Language Models are not Fair Evaluatorscited by 1,050
- 2023The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)cited by 677
- 2023Large Language Models Are Not Robust Multiple Choice Selectorscited by 454
- 2024GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generationcited by 151
- 2024OpenSkill: A faster asymmetric multi-team, multiplayer rating systemcited by 9
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.