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mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding by Hu, Anwen; Xu, Haiyang; Zhang, Liang; Ye, Jiabo; Yan, Ming; Zhang, Ji; Jin, Qin; Huang, Fei; Zhou, Jingren is a scholarly article available to read on EtoBox.

What is mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding about?

Multimodel Large Language Models(MLLMs) have achieved promising OCR-free Document Understanding performance by increasing the supported resolution of document images. However, this comes at the cost of generating thousands of visual tokens for a single document image, leading to excessive GPU memory and slower inference times, particularly in multi-page document comprehension. In this work, to address these challenges, we propose a High-resolution DocCompressor module to compress each high-resolution document image into 324 tokens, guided by low-resolution global visual features. With this compression module, to strengthen multi-page document comprehension ability and balance both token efficiency and question-answering performance, we develop the DocOwl2 under a three-stage training framework: Single-image Pretraining, Multi-image Continue-pretraining, and Multi-task Finetuning. DocOwl2 sets a new state-of-the-art across multi-page document understanding benchmarks and reduces first token latency by more than 50%, demonstrating advanced capabilities in multi-page questioning answering, explanation with evidence pages, and cross-page structure understanding. Additionally, compared

Author

Hu, Anwen; Xu, Haiyang; Zhang, Liang; Ye, Jiabo; Yan, Ming; Zhang, Ji; Jin, Qin; Huang, Fei; Zhou, Jingren

Published

2024

Language

EN