Efficient Multi-Page Document Visual Question Answering using Self-Attention Scoring Mechanism

Core Concepts

A novel self-attention scoring mechanism that efficiently adapts single-page Document VQA models to multi-page scenarios without constraints on the number of pages.

Abstract

The paper proposes a method for multi-page Document Visual Question Answering (VQA) that leverages a self-attention scoring mechanism to determine the relevance of each document page for a given question. The key highlights are:

The method utilizes a visual-only document representation, bypassing the need for Optical Character Recognition (OCR) annotations. It employs the encoder from the Pix2Struct document understanding model to encode both the question and page context as a unified visual feature space.

The self-attention scoring module is trained to generate relevance scores for each document page based on the question, enabling the retrieval of pertinent pages. This allows the method to extend single-page Document VQA models to multi-page scenarios without constraints on the number of pages during evaluation.

The training scheme involves an efficient strategy where only one positive page and one randomly selected negative page of a document are incorporated for training the proposed scoring mechanism.

Experiments demonstrate that the proposed method achieves state-of-the-art performance on the MP-DocVQA dataset without the need for OCR, and sustains satisfactory performance in extended scenarios with documents of up to 793 pages, compared to a maximum of 20 pages in the original dataset.

Stats

The maximum number of pages in the original MP-DocVQA dataset is 20.
The extended test set includes documents with up to 793 pages.

Quotes

"Our method demonstrates satisfactory performance in this extended evaluation."
"The increase in the number of pages is not implemented on the training set, so that we can maintain a fair comparison with the state of the art without introducing additional information during training."

Key Insights Distilled From

Multi-Page Document Visual Question Answering using Self-Attention Scoring Mechanism

by Lei ... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19024.pdf

Deeper Inquiries

How can the proposed self-attention scoring mechanism be further improved to handle even longer documents with thousands of pages?

The proposed self-attention scoring mechanism can be enhanced to handle longer documents with thousands of pages by implementing the following strategies:

Hierarchical Attention: Introduce a hierarchical attention mechanism that operates at different levels of granularity. This way, the model can first attend to high-level document sections and then focus on specific pages within those sections. By hierarchically attending to different levels of information, the model can efficiently process longer documents.

Sparse Attention: Implement a sparse attention mechanism that dynamically selects a subset of relevant pages to attend to based on the question. This way, the model can avoid processing all pages simultaneously, reducing computational complexity and memory requirements for longer documents.

Memory Augmented Networks: Incorporate memory augmented networks to store and retrieve relevant information from previous pages as the model processes through the document. This can help the model maintain context across multiple pages and improve performance on longer documents.

Dynamic Page Chunking: Develop a dynamic page chunking strategy where the document is divided into smaller chunks or segments based on content similarity or structural cues. The model can then attend to these chunks sequentially, effectively handling longer documents without overwhelming the system.

Incremental Processing: Implement an incremental processing approach where the model processes a fixed number of pages at a time, updating its internal state before moving on to the next set of pages. This incremental processing can help the model maintain efficiency and effectiveness on documents with thousands of pages.

By incorporating these advanced techniques, the self-attention scoring mechanism can be further optimized to handle longer documents with thousands of pages while maintaining performance and efficiency.

How can the proposed approach be extended to handle multi-modal information beyond just text and images, such as tables, diagrams, and handwritten annotations, in the context of multi-page document understanding?

To extend the proposed approach to handle multi-modal information beyond text and images, such as tables, diagrams, and handwritten annotations, in the context of multi-page document understanding, the following strategies can be employed:

Multi-Modal Fusion: Implement a multi-modal fusion mechanism that integrates information from different modalities, including text, images, tables, diagrams, and handwritten annotations. This fusion can be achieved through techniques like cross-modal attention or late fusion methods to combine features from diverse sources effectively.

Modality-Specific Encoders: Develop modality-specific encoders tailored for processing tables, diagrams, and handwritten annotations. Each modality can have its dedicated encoder to extract relevant features and representations, which are then integrated into the overall multi-modal framework.

Graph Neural Networks: Utilize graph neural networks to model the relationships and dependencies between different modalities within the document. This approach can capture complex interactions between text, images, tables, and diagrams, enabling a more comprehensive understanding of multi-modal content.

Attention Mechanisms for Different Modalities: Implement attention mechanisms specific to each modality to focus on relevant parts of the document. For example, attention mechanisms can be designed to attend to specific regions in images, cells in tables, or shapes in diagrams, enhancing the model's ability to extract meaningful information.

Data Augmentation and Pre-Training: Augment the dataset with diverse multi-modal examples and pre-train the model on a wide range of multi-modal tasks to improve its ability to understand and reason across different types of content. This pre-training can help the model generalize better to new multi-modal inputs.

By incorporating these strategies, the proposed approach can be extended to effectively handle multi-modal information beyond text and images, enabling comprehensive document understanding that includes tables, diagrams, handwritten annotations, and other types of content.