People are spending an enormous amount of time on digital devices through
graphical user interfaces (GUIs), e.g., computer or smartphone screens. Large
language models (LLMs) such as ChatGPT can assist people in tasks like writing
emails, but struggle to understand and interact with GUIs, thus limiting their
potential to increase automation levels. In this paper, we introduce CogAgent,
an 18-billion-parameter visual language model (VLM) specializing in GUI
understanding and navigation. By utilizing both low-resolution and
high-resolution image encoders, CogAgent supports input at a resolution of
1120*1120, enabling it to recognize tiny page elements and text. As a
generalist visual language model, CogAgent achieves the state of the art on
five text-rich and four general VQA benchmarks, including VQAv2, OK-VQA,
Text-VQA, ST-VQA, ChartQA, infoVQA, DocVQA, MM-Vet, and POPE. CogAgent, using
only screenshots as input, outperforms LLM-based methods that consume extracted
HTML text on both PC and Android GUI navigation tasks -- Mind2Web and AITW,
advancing the state of the art. The model and codes are available at
https://github.com/THUDM/CogVLM.

本研究介绍了 CogAgent，这是一个在 GUI 理解和导航方面专门使用的 180 亿参数的视觉语言模型，通过低分辨率和高分辨率图像编码器，支持 1120*1120 分辨率的输入，能够识别微小的页面元素和文本。CogAgent 在五个文本丰富和四个通用的 VQA 基准测试中达到了最新的技术水平，在 PC 和 Android 的 GUI 导航任务中超过了 LLM 方法，从而推动了技术的发展。