TextMonkey is a large multimodal model tailored for text-centric tasks, introducing enhancements like Shifted Window Attention and Token Resampler. By improving cross-window connectivity and reducing redundant tokens, TextMonkey achieves significant performance boosts in various benchmarks related to document understanding.
The paper discusses the challenges of text-heavy tasks like document question answering and fine-grained text analysis. It introduces TextMonkey, which utilizes innovative techniques to improve performance across different datasets. The model's ability to understand spatial relationships, reduce hallucinations, and support clicking screenshots is highlighted as key features.
Through an ablation study, the effectiveness of strategies like zero initialization and token resampling is demonstrated. The importance of incorporating position information for improved performance is also discussed. Additionally, the interaction between input resolution and the number of tokens remained is explored to optimize model performance.
The paper concludes by discussing the potential applications of TextMonkey in structuring charts and tables into JSON format. The model's capabilities in acting as an app agent for smartphone applications are also highlighted, showcasing its versatility beyond traditional document understanding tasks.
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by Yuliang Liu,... at arxiv.org 03-08-2024
https://arxiv.org/pdf/2403.04473.pdfDeeper Inquiries