mlsanityData to 7 Oct 2026

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

arXiv 2402.17983 ↗First posted 28 Feb 2024cs.CL cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 21 references that are in the map. First posted on arXiv 28 Feb 2024.

Builds on

16 of 21 references.

References · 16

  1. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
  2. 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,291
  3. 2019LayoutLM: Pre-training of Text and Layout for Document Image Understandingcited by 549
  4. 2022LayoutLMv3: Pre-training for Document AI with Unified Text and Image Maskingcited by 481
  5. 2022Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understandingcited by 360
  6. 2019PubLayNet: largest dataset ever for document layout analysiscited by 349
  7. 2019FUNSD: A Dataset for Form Understanding in Noisy Scanned Documentscited by 345
  8. 2021Towards Understanding Knowledge Distillationcited by 158
  9. 2022LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understandingcited by 107
  10. 2021BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documentscited by 104
  11. 2021Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solutioncited by 64
  12. 2023FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extractioncited by 20
4 more
  1. 2019Visual Question Answering using Deep Learning: A Survey and Performance Analysiscited by 19
  2. 2022V-Doc : Visual questions answers with Documentscited by 19
  3. 2022Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysiscited by 17
  4. 2023Fast-StrucTexT: An Efficient Hourglass Transformer with Modality-guided Dynamic Token Merge for Document Understandingcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.