title: FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich
    Document Understanding
summary: In this work, we propose Few Shot Domain Adapting Graph (FS-DAG), a scalable
  and efficient model architecture for visually rich document understanding
  (VRDU) in few-shot settings. FS-DAG leverages domain-specific and
  language/vision specific backbones within a modular framework to adapt to
  diverse document types with minimal data. The model is robust to practical
  challenges such as handling OCR errors, misspellings, and domain shifts, which
  are critical in real-world deployments. FS-DAG is highly performant with less
  than 90M parameters, making it well-suited for complex real-world applications
  for Information Extraction (IE) tasks where computational resources are
  limited. We demonstrate FS-DAG's capability through extensive experiments for
  information extraction task, showing significant improvements in convergence
  speed and performance compared to state-of-the-art methods. Additionally, this
  work highlights the ongoing progress in developing smaller, more efficient
  models that do not compromise on performance. Code :
  https://github.com/oracle-samples/fs-dag
paper_authors._id: 68389760cf258e24a6079f6a
paper_authors.name: Amit Agarwal
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-30T23:03:04.452Z
paper_authors._id: 6321a50a6b1992383fa00db1
paper_authors.avatarUrl: /avatars/92311b4a5e8bcc8f5477747621334fc8.svg
paper_authors.fullname: Amit Agarwal
paper_authors.type: user
paper_authors.user: amitbcp
paper_authors._id: 68389760cf258e24a6079f6b
paper_authors.name: Srikant Panda
paper_authors._id: 68389760cf258e24a6079f6c
paper_authors.name: Kulbhushan Pachauri
paper_title: FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich
    Document Understanding
paper_summary: In this work, we propose Few Shot Domain Adapting Graph (FS-DAG), a scalable
  and efficient model architecture for visually rich document understanding
  (VRDU) in few-shot settings. FS-DAG leverages domain-specific and
  language/vision specific backbones within a modular framework to adapt to
  diverse document types with minimal data. The model is robust to practical
  challenges such as handling OCR errors, misspellings, and domain shifts, which
  are critical in real-world deployments. FS-DAG is highly performant with less
  than 90M parameters, making it well-suited for complex real-world applications
  for Information Extraction (IE) tasks where computational resources are
  limited. We demonstrate FS-DAG's capability through extensive experiments for
  information extraction task, showing significant improvements in convergence
  speed and performance compared to state-of-the-art methods. Additionally, this
  work highlights the ongoing progress in developing smaller, more efficient
  models that do not compromise on performance. Code :
  https://github.com/oracle-samples/fs-dag
paper_ai_summary: FS-DAG, a modular model architecture, efficiently adapts to diverse document types with few-shot learning for visually rich document understanding in resource-constrained environments.
paper_ai_keywords: Few Shot Domain Adapting Graph FS-DAG visually rich document understanding VRDU domain-specific backbones language/vision specific backbones domain shifts OCR errors misspellings Information Extraction IE parameter-efficient