Visually Guided Generative Text-Layout Pre-training for Document Intelligence

Core Concepts

ViTLP proposes visually guided generative text-layout pre-training to enhance document understanding by optimizing hierarchical language and layout modeling objectives.

Abstract

The content introduces ViTLP, a model for document intelligence pre-training. It discusses the importance of pre-training techniques in boosting visual document understanding. ViTLP optimizes text-layout generation from document images, addressing limitations in processing long documents. The model is evaluated on various VDU tasks, showing competitive performance over existing baselines.

1. Introduction

   • Pre-training techniques boost visual document understanding.
   • ViTLP optimizes hierarchical language and layout modeling.

2. Approach

   • ViTLP employs an encoder-decoder framework.
   • Global-to-local text-layout generation process is designed.

3. Multi-segment Pre-training Scheme

   • Divides long sequences into segments for efficient processing.

4. Applications of ViTLP

   • OCR Text Localization and Recognition: ViTLP functions as a native OCR model.
   • Downstream VDU Tasks: Achieves superior performance on information extraction, document classification, and VQA tasks.

5. Experiments

   • Evaluation on FUNSD, CORD, and RVL-CDIP datasets shows competitive performance.

6. Ablation Study

   • Removing layout modeling or hierarchical modeling leads to performance drops.

7. Generative Document VQA Results

   • ViTLP outperforms DONUT on InfographicVQA but underperforms on DocVQA.

8. Related Work

   • Discusses OCR-based methods, OCR-free methods, LLM-backbone methods in visual document processing research.

Stats

"ViTLP achieves the 95.59% F1 score on CORD information extraction."
"ViTLP achieves the 95.36% accuracy on RVL-CDIP document classification."

Quotes

"ViTLP can function as a native OCR model to localize and recognize texts of document images."
"Extensive experiments show that ViTLP achieves competitive performance over existing baselines on benchmark VDU tasks."

Key Insights Distilled From

Visually Guided Generative Text-Layout Pre-training for Document Intelligence

by Zhiming Mao,... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16516.pdf

Deeper Inquiries

How does the multi-segment pre-training scheme impact the efficiency of ViTLP?

The multi-segment pre-training scheme in ViTLP plays a crucial role in enhancing the efficiency of the model. By dividing long document sequences into multiple segments, ViTLP can process documents of arbitrary length without computational constraints. This segmentation allows for more manageable processing and generation of text-layout sequences, ensuring that the model can handle extensive textual information efficiently. Additionally, by using prefix tokens from previous segments as prompts for generating subsequent tokens, ViTLP maintains context continuity across different segments, enabling seamless processing of long documents.

What are the potential limitations of using a smaller-scale pre-trained model like ViTLP?

While ViTLP offers significant advantages in visual document processing tasks, being a smaller-scale pre-trained model may pose certain limitations. One potential limitation is related to scalability and generalization. Larger models often have more parameters and capacity to capture complex patterns and nuances in data compared to smaller models like ViTLP. This could result in slightly lower performance on tasks requiring extensive reasoning or understanding intricate document structures.
Another limitation could be related to fine-tuning on specific datasets or domains. Smaller models may not have been exposed to diverse enough data during pre-training, which could affect their adaptability and performance when fine-tuned on specialized tasks or datasets with unique characteristics.
Lastly, interpretability might be another area where smaller-scale models like ViTLP could face challenges compared to larger models with more sophisticated architectures. Interpreting decisions made by smaller models might require additional effort due to potentially limited representation capabilities.

How does the grounding capability of ViTLP contribute to its interpretability in VQA tasks?

ViTLP's grounding capability significantly enhances its interpretability in Visual Question Answering (VQA) tasks by providing visual rationales for generated answers. The ability to predict regions of interest (ROIs) associated with generated answers makes the answer generation process more transparent and understandable for humans reviewing the outputs.
By including layout coordinates along with answer words as part of its output sequence, ViTPL generates interpretable results that show precisely where each word contributing to an answer is located within a document image. These visual groundings serve as evidence supporting each generated answer's relevance and accuracy while also offering insights into how the model arrived at its conclusions based on specific regions within an image.
Overall, this grounding capability adds a layer of transparency and trustworthiness to ViTPL's VQA outputs by making them visually verifiable through explicit references to corresponding locations within images.