Self-supervised pretraining has been able to produce transferable
representations for various visual document understanding (VDU) tasks. However,
the ability of such representations to adapt to new distribution shifts at
test-time has not been studied yet. We propose DocTTA, a novel test-time
adaptation approach for documents that leverages cross-modality self-supervised
learning via masked visual language modeling as well as pseudo labeling to
adapt models learned on a \textit{source} domain to an unlabeled
\textit{target} domain at test time. We also introduce new benchmarks using
existing public datasets for various VDU tasks including entity recognition,
key-value extraction, and document visual question answering tasks where DocTTA
improves the source model performance up to 1.79\% in (F1 score), 3.43\% (F1
score), and 17.68\% (ANLS score), respectively while drastically reducing
calibration error on target data.

文章介绍了一种新的文档测试时间自适应方法，利用跨模态自监督学习和伪标记来使源域的模型适应测试时未标记的目标域，并在各种视觉文档理解任务中提升效果。