Evaluating creativity is challenging, even for humans, not only because of its subjectivity but also because it involves complex cognitive processes. Inspired by work in marketing, we attempt to break down visual advertisement creativity into atypicality and originality. With fine-grained human annotations on these dimensions, we propose a suite of tasks specifically for such a subjective problem. We also evaluate the alignment between state-of-the-art (SoTA) vision language models (VLMs) and humans on our proposed benchmark, demonstrating both the promises and challenges of using VLMs for automatic creativity assessment.
@article{arxiv.2503.00046,
title = {Leveraging Large Models to Evaluate Novel Content: A Case Study on Advertisement Creativity},
author = {Zhaoyi Joey Hou and Adriana Kovashka and Xiang Lorraine Li},
journal= {arXiv preprint arXiv:2503.00046},
year = {2025}
}