面向语言智能的可扩展视觉预训练

摘要

大型基础模型的快速进步主要依赖于在大规模文本语料库上的预训练。然而，许多知识通过视觉表征传递——图表、排版方程和页面布局承载着丰富的信息，这些信息仅凭文本无法准确或完整地捕捉。然而，当前的预训练方法丢弃了这些视觉线索，将文档和网页等视觉丰富的源材料转换为纯文本，以学习语言智能。本文挑战了语言模型必须在纯文本表示上训练这一默认假设，并证明视觉预训练是基础模型智能的一种可扩展学习方法。为此，我们对无需文本提取而直接利用视觉文档的无监督视觉预训练范式进行了系统研究。在多种骨干网络和基准测试中，对相同底层语料库进行的视觉预训练始终优于纯文本预训练，为可扩展的语言智能提供了一条高效路径。

English

The rapid progress of large foundation models has been driven predominantly by pretraining on large-scale text corpora. However, many forms of knowledge are conveyed through visual representations, where figures, typeset equations, and page layouts carry rich information that cannot be faithfully or completely captured by text alone. Yet current pretraining approaches discard these visual cues by converting visually rich sources, such as documents and web pages, into plain text for learning language intelligence. This paper challenges the default assumption that language models must be trained on text-only representations and shows that Visual Pretraining is a scalable learner for foundation model intelligence. To this end, we conduct a systematic study of unsupervised visual pretraining paradigms that directly leverage visual documents without text extraction. Across multiple backbones and benchmarks, visual pretraining on the same underlying corpora consistently outperforms text-only pretraining, offering an efficient pathway to scalable language intelligence.

面向语言智能的可扩展视觉预训练

Scalable Visual Pretraining for Language Intelligence

摘要

Support