Test-Time Adaptation for Visual Document Understanding
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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 source domain to an unlabeled 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.
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