Recently, large language models (LLMs) have made significant advancements in
natural language understanding and generation. However, their potential in
computer vision remains largely unexplored. In this paper, we introduce a new,
exploratory approach that enables LLMs to process images using the Scalable
Vector Graphics (SVG) format. By leveraging the XML-based textual descriptions
of SVG representations instead of raster images, we aim to bridge the gap
between the visual and textual modalities, allowing LLMs to directly understand
and manipulate images without the need for parameterized visual components. Our
method facilitates simple image classification, generation, and in-context
learning using only LLM capabilities. We demonstrate the promise of our
approach across discriminative and generative tasks, highlighting its (i)
robustness against distribution shift, (ii) substantial improvements achieved
by tapping into the in-context learning abilities of LLMs, and (iii) image
understanding and generation capabilities with human guidance. Our code, data,
and models can be found here this https URL

本文介绍了一种新的、探索性的方法，使大型语言模型能够使用可缩放矢量图形（SVG）格式来处理图像。我们的方法旨在弥合视觉和文本模态之间的鸿沟，允许 LLM 直接理解和操作图像，而无需参数化的视觉组件。通过简单的图像分类、生成和上下文学习，我们展示了我们方法的潜力在鉴别性和生成性任务上，强调了其对分布偏移的鲁棒性以及通过利用 LLM 的上下文学习能力实现的重大提高。