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3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding

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arxiv 2402.17983 v3 pith:VB3W3AHL submitted 2024-02-28 cs.CL cs.CV

classification cs.CLcs.CV
keywords formunderstandingdocumentdocumentsmodelmulti-teacherdistillationknowledge
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents a groundbreaking multimodal, multi-task, multi-teacher joint-grained knowledge distillation model for visually-rich form document understanding. The model is designed to leverage insights from both fine-grained and coarse-grained levels by facilitating a nuanced correlation between token and entity representations, addressing the complexities inherent in form documents. Additionally, we introduce new inter-grained and cross-grained loss functions to further refine diverse multi-teacher knowledge distillation transfer process, presenting distribution gaps and a harmonised understanding of form documents. Through a comprehensive evaluation across publicly available form document understanding datasets, our proposed model consistently outperforms existing baselines, showcasing its efficacy in handling the intricate structures and content of visually complex form documents.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth

    cs.LG 2026-05 conditional novelty 5.0 of 10

    OCR tools can be ranked without ground-truth labels by measuring how much a multimodal LLM must correct each tool's output.

  2. VRD-IU: Lessons from Visually Rich Document Intelligence and Understanding

    cs.CV 2025-06 conditional novelty 5.0 of 10

    The VRD-IU competition results show that hierarchical decomposition and pretrained multimodal transformers are effective for form key-information extraction and localization, but the paper lacks baseline comparisons a...

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