The paper introduces a novel model for understanding visually-rich form documents by leveraging multi-teacher knowledge distillation. It outperforms existing baselines across various datasets, showcasing its efficacy in handling complex structures and content of visually complex form documents.
The complexity of form document understanding arises from the involvement of two distinct authors in a form and the integration of diverse visual cues. Traditional models do not account for the diverse carriers of document versions and their associated noises, exacerbating challenges in understanding form structures and components.
The proposed model incorporates multiple teachers from different tasks to create more inclusive and representative multi- and joint-grained document representations. By integrating inter-grained and cross-grained loss functions, it refines the knowledge distillation transfer process, enhancing the overall effectiveness of downstream tasks related to document understanding.
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