Summary of Docmamba: Efficient Document Pre-training with State Space Model, by Pengfei Hu et al.
DocMamba: Efficient Document Pre-training with State Space Model
by Pengfei Hu, Zhenrong Zhang, Jiefeng Ma, Shuhang Liu, Jun Du, Jianshu Zhang
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, DocMamba, is a novel approach to visually-rich document understanding that addresses the limitations of transformer-based models by reducing computational complexity while preserving global modeling capabilities. This is achieved through the use of state space models and the Segment-First Bidirectional Scan (SFBS) mechanism. Experimental results demonstrate DocMamba’s effectiveness on downstream datasets such as FUNSD, CORD, and SORIE, achieving new state-of-the-art results while improving speed and reducing memory usage. The framework also shows potential for length extrapolation on the HRDoc dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to understand documents that are rich in images and text. This approach uses a special type of model called DocMamba, which is faster and more efficient than other models. It does this by breaking down long documents into smaller chunks and processing them one at a time. The team tested their approach on several different datasets and found that it was able to understand the documents better than other models. This new way of understanding documents could be useful for many applications, such as searching through large collections of documents or summarizing important information. |
Keywords
» Artificial intelligence » Transformer
