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.

通过使用遥感图像领域的 RSICD 数据集中提供的字幕，本文旨在解决字幕数据集中可能存在的信息和沟通缺陷问题，并通过进行 ChatGPT 语法纠正来增加字幕模型的性能准确性。

卫星字幕：大规模语言模型助力标注

Satellite Captioning: Large Language Models to Augment Labeling

Over the past decade, the gender pay gap has remained steady with women
earning 84 cents for every dollar earned by men on average. Many studies
explain this gap through demand-side bias in the labor market represented
through employers' job postings. However, few studies analyze potential bias
from the worker supply-side. Here, we analyze the language in millions of US
workers' resumes to investigate how differences in workers' self-representation
by gender compare to differences in earnings. Across US occupations, language
differences between male and female resumes correspond to 11% of the variation
in gender pay gap. This suggests that females' resumes that are semantically
similar to males' resumes may have greater wage parity. However, surprisingly,
occupations with greater language differences between male and female resumes
have lower gender pay gaps. A doubling of the language difference between
female and male resumes results in an annual wage increase of $2,797 for the
average female worker. This result holds with controls for gender-biases of
resume text and we find that per-word bias poorly describes the variance in
wage gap. The results demonstrate that textual data and self-representation are
valuable factors for improving worker representations and understanding
employment inequities.

通过分析数百万个美国工人的简历，研究了女性和男性简历的语言差异与性别薪资差距的关系，发现女性简历与男性简历在语言上的差异占据了性别薪资差距的 11％，而与男性简历语言差异越大的职位，女性的薪资差距越小。