Textbooks are the primary vehicle for delivering quality education to
students. It has been shown that explanatory or illustrative visuals play a key
role in the retention, comprehension and the general transfer of knowledge.
However, many textbooks, especially in the developing world, are low quality
and lack interesting visuals to support student learning. In this paper, we
investigate the effectiveness of vision-language models to automatically
enhance textbooks with images from the web. Specifically, we collect a dataset
of e-textbooks from one of the largest free online publishers in the world. We
rigorously analyse the dataset, and use the resulting analysis to motivate a
task that involves retrieving and appropriately assigning web images to
textbooks, which we frame as a novel optimization problem. Through a
crowd-sourced evaluation, we verify that (1) while the original textbook images
are rated higher, automatically assigned ones are not far behind, and (2) the
choice of the optimization problem matters. We release the dataset of textbooks
with an associated image bank to spur further research in this area.

本文研究了使用视觉 - 语言模型自动从网络中获取图片来增强教科书的有效性，将该任务作为一种新的优化问题。经过众包评估验证了该方法的可行性，并发布了相应的数据集以促进更多的研究。