Satellite Captioning: Large Language Models to Augment Labeling
Grant Rosario David Noever
With the growing capabilities of modern object detection networks and
datasets to train them, it has gotten more straightforward and, importantly,
less laborious to get up and running with a model that is quite adept at
detecting any number of various objects. However, while image datasets for
object detection have grown and continue to proliferate (the current most
extensive public set, ImageNet, contains over 14m images with over 14m
instances), the same cannot be said for textual caption datasets. While they
have certainly been growing in recent years, caption datasets present a much
more difficult challenge due to language differences, grammar, and the time it
takes for humans to generate them. Current datasets have certainly provided
many instances to work with, but it becomes problematic when a captioner may
have a more limited vocabulary, one may not be adequately fluent in the
language, or there are simple grammatical mistakes. These difficulties are
increased when the images get more specific, such as remote sensing images.
This paper aims to address this issue of potential information and
communication shortcomings in caption datasets. To provide a more precise
analysis, we specify our domain of images to be remote sensing images in the
RSICD dataset and experiment with the captions provided here. Our findings
indicate that ChatGPT grammar correction is a simple and effective way to
increase the performance accuracy of caption models by making data captions
more diverse and grammatically correct.