  This paper does not present a novel method. Instead, it delves into an
essential, yet must-know baseline in light of the latest advancements in
Generative Artificial Intelligence (GenAI): the utilization of GPT-4 for visual
understanding. Our study centers on the evaluation of GPT-4's linguistic and
visual capabilities in zero-shot visual recognition tasks: Firstly, we explore
the potential of its generated rich textual descriptions across various
categories to enhance recognition performance without any training. Secondly,
we evaluate GPT-4's visual proficiency in directly recognizing diverse visual
content. We conducted extensive experiments to systematically evaluate GPT-4's
performance across images, videos, and point clouds, using 16 benchmark
datasets to measure top-1 and top-5 accuracy. Our findings show that GPT-4,
enhanced with rich linguistic descriptions, significantly improves zero-shot
recognition, offering an average top-1 accuracy increase of 7% across all
datasets. GPT-4 excels in visual recognition, outshining OpenAI-CLIP's ViT-L
and rivaling EVA-CLIP's ViT-E, particularly in video datasets HMDB-51 and
UCF-101, where it leads by 22% and 9%, respectively. We hope this research
contributes valuable data points and experience for future studies. We release
our code at https://github.com/whwu95/GPT4Vis.
