Practical Solutions for Document Understanding
Introducing DocOwl2: A High-Resolution Compression Architecture
Understanding multi-page documents and news videos is a common task in human daily life. To address this, Multimodal Large Language Models (MLLMs) need to understand multiple images with rich visually-situated text information.
Existing approaches to comprehend document images have limitations due to the large number of visual tokens required to represent a single document image, resulting in long inference times and significant GPU memory usage.
Researchers from Alibaba Group and Renmin University of China have proposed a robust compressing architecture called High-resolution DocCompressor. This method utilizes the visual features of a global low-resolution image to effectively capture the overall layout information of the document.
The DocOwl2 model, incorporating the High-resolution DocCompressor, achieves efficient OCR-free Multi-page Document Understanding by compressing each high-resolution document image into just 324 tokens using cross-attention with global visual features as guidance. It outperforms existing compressing methods and matches state-of-the-art MLLMs while using fewer visual tokens.
Value of DocOwl2
DocOwl2 demonstrates superior performance and significantly lower First Token Latency compared to other Multimodal LLMs, making it an efficient solution for multi-page document understanding and text-rich video understanding tasks.
Using thousands of visual tokens per document page is often redundant and a waste of computational resources. DocOwl2 aims to bring attention to balancing efficient image representation and high-performance document understanding.
AI Solutions for Business
If you want to evolve your company with AI, stay competitive, and optimize your document understanding, consider leveraging DocOwl2. AI can redefine your way of work by identifying automation opportunities, defining KPIs, selecting suitable AI solutions, and implementing them gradually.
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