  In this paper, we evaluate different abilities of GPT-4V including visual
understanding, language understanding, visual puzzle solving, and understanding
of other modalities such as depth, thermal, video, and audio. To estimate
GPT-4V's performance, we manually construct 656 test instances and carefully
evaluate the results of GPT-4V. The highlights of our findings are as follows:
(1) GPT-4V exhibits impressive performance on English visual-centric benchmarks
but fails to recognize simple Chinese texts in the images; (2) GPT-4V shows
inconsistent refusal behavior when answering questions related to sensitive
traits such as gender, race, and age; (3) GPT-4V obtains worse results than
GPT-4 (API) on language understanding tasks including general language
understanding benchmarks and visual commonsense knowledge evaluation
benchmarks; (4) Few-shot prompting can improve GPT-4V's performance on both
visual understanding and language understanding; (5) GPT-4V struggles to find
the nuances between two similar images and solve the easy math picture puzzles;
(6) GPT-4V shows non-trivial performance on the tasks of similar modalities to
image, such as video and thermal. Our experimental results reveal the ability
and limitations of GPT-4V and we hope our paper can provide some insights into
the application and research of GPT-4V.

Computation and Language;Computer Vision and Pattern Recognition