DocMamba: Efficient Document Pre-training with State Space Model (2409.11887v2)

Abstract: In recent years, visually-rich document understanding has attracted increasing attention. Transformer-based pre-trained models have become the mainstream approach, yielding significant performance gains in this field. However, the self-attention mechanism's quadratic computational complexity hinders their efficiency and ability to process long documents. In this paper, we present DocMamba, a novel framework based on the state space model. It is designed to reduce computational complexity to linear while preserving global modeling capabilities. To further enhance its effectiveness in document processing, we introduce the Segment-First Bidirectional Scan (SFBS) to capture contiguous semantic information. Experimental results demonstrate that DocMamba achieves new state-of-the-art results on downstream datasets such as FUNSD, CORD, and SORIE, while significantly improving speed and reducing memory usage. Notably, experiments on the HRDoc confirm DocMamba's potential for length extrapolation.

• The paper introduces DocMamba, a novel SSM-based approach that achieves linear computational complexity for processing long documents.
• It utilizes a Segment-First Bidirectional Scan to capture contiguous semantic information in complex two-dimensional layouts.
• Experiments on datasets like FUNSD, CORD, and HRDoc demonstrate state-of-the-art performance with significantly lower computational costs.

DocMamba: Efficient Document Pre-training with State Space Model

The paper introduces DocMamba, a novel framework centered on the State Space Model (SSM) for visually-rich document understanding, an area traditionally dominated by transformer-based models. The authors address the computational inefficiency inherent in transformers due to their quadratic complexity with respect to input length, proposing DocMamba to achieve linear complexity while maintaining robust global modeling capabilities.

Core Contributions

1. State Space Models for Document Understanding: Departing from transformer architectures, DocMamba utilizes SSMs to process documents. SSMs provide linear computational complexity, which significantly reduces the resource requirements for processing long documents. This makes DocMamba particularly advantageous for tasks involving lengthy document sequences, where traditional models like LayoutLM and LayoutLMv2 struggled due to their context length limitations.
2. Segment-First Bidirectional Scan (SFBS): To capture contiguous semantic information in documents, the authors propose SFBS. This method ensures that tokens belonging to the same document segment are processed together, enhancing DocMamba's capability to understand documents' complex two-dimensional layouts. The bidirectional nature of the scan allows for more comprehensive context capturing, akin to what bidirectional approaches have provided in other natural language tasks.
3. Performance and Efficiency: Experimental validations show that DocMamba achieves state-of-the-art results on established datasets including FUNSD, CORD, and SROIE. Beyond performance gains, DocMamba also exhibits significant reductions in computational time and memory usage compared to traditional models. This is empirically demonstrated by experiments on the HRDoc dataset, where DocMamba shows promising extension in handling longer input lengths due to its inherent position adaptability.
4. Potential for Length Extrapolation: A notable advantage of DocMamba is its potential for length extrapolation. The model's architecture inherently supports processing sequences longer than those seen during training, without the need for additional position embeddings that transformers typically require.

Implications and Future Directions

DocMamba's introduction presents several implications for both practical applications and theoretical advancements in document understanding. From a practical perspective, the efficiency of DocMamba can dramatically reduce computational costs in real-world applications, such as automated form processing and information extraction from receipts. The model's linear scalability addresses one of the bottlenecks in deploying AI solutions across large-scale document datasets prevalent in various industries.

Theoretically, DocMamba's success lays a foundation for further exploration into SSMs' applicability in other domains traditionally dominated by transformers. The findings suggest that, for tasks with extended sequential dependencies and structured input, SSMs could offer a competitive alternative.

Conclusions

In conclusion, DocMamba leverages the strengths of State Space Models to address the inefficiencies in transformer-based approaches for document understanding. By efficiently capturing the semantic and structural information in complex document layouts, DocMamba sets a new benchmark in the field. Future work could explore integrating additional modalities, such as image information, which could enhance the model's performance and versatility. The idea of using SSMs in multimodal contexts holds promise for enhancing the depth and breadth of AI applications across various domains.