Test-Time Adaptation for Visual Document Understanding

Sayna Ebrahimi, Sercan O Arik, Tomas Pfister

Keywords: Test-time adaptation, source data-free domain adaptation, visual document understanding

TL;DR: Proposing a novel test-time adaptation approach and three benchmarks for visual document understanding via masked language modeling and pseudo labeling.

Abstract: 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.

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Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2206.07240/code)