FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich Document Understanding
This work addresses the challenge of information extraction from documents with limited data for real-world applications, representing an incremental advancement in efficient model design.
The paper tackles the problem of visually rich document understanding in few-shot settings by proposing FS-DAG, a scalable and efficient model architecture that adapts to diverse document types with minimal data, showing significant improvements in convergence speed and performance compared to state-of-the-art methods.
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