  Interest is increasing among political scientists in leveraging the extensive
information available in images. However, the challenge of interpreting these
images lies in the need for specialized knowledge in computer vision and access
to specialized hardware. As a result, image analysis has been limited to a
relatively small group within the political science community. This landscape
could potentially change thanks to the rise of large language models (LLMs).
This paper aims to raise awareness of the feasibility of using Gemini for image
content analysis. A retrospective analysis was conducted on a corpus of 688
images. Content reports were elicited from Gemini for each image and then
manually evaluated by the authors. We find that Gemini is highly accurate in
performing object detection, which is arguably the most common and fundamental
task in image analysis for political scientists. Equally important, we show
that it is easy to implement as the entire command consists of a single prompt
in natural language; it is fast to run and should meet the time budget of most
researchers; and it is free to use and does not require any specialized
hardware. In addition, we illustrate how political scientists can leverage
Gemini for other image understanding tasks, including face identification,
sentiment analysis, and caption generation. Our findings suggest that Gemini
and other similar LLMs have the potential to drastically stimulate and
accelerate image research in political science and social sciences more
broadly.
