FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich
  Document Understanding
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
68389760cf258e24a6079f6a
Amit Agarwal
extracted_confirmed
2025-05-30T23:03:04.452Z
6321a50a6b1992383fa00db1
/avatars/92311b4a5e8bcc8f5477747621334fc8.svg
Amit Agarwal
user
amitbcp
68389760cf258e24a6079f6b
Srikant Panda
68389760cf258e24a6079f6c
Kulbhushan Pachauri
FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich
  Document Understanding
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
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.
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