HRVDA: High-Resolution Visual Document Assistant

• URL: http://arxiv.org/abs/2404.06918v1
• Date: Wed, 10 Apr 2024 11:10:50 GMT
• Title: HRVDA: High-Resolution Visual Document Assistant
• Authors: Chaohu Liu, Kun Yin, Haoyu Cao, Xinghua Jiang, Xin Li, Yinsong Liu, Deqiang Jiang, Xing Sun, Linli Xu,
• Abstract summary: We propose a High-Resolution Visual Document Assistant (HRVDA) to bridge the gap between MLLMs and visual document understanding. HRVDA employs a content filtering mechanism and an instruction filtering module to filter out the content-agnostic visual tokens and instruction-agnostic visual tokens. Our model achieves state-of-the-art performance across multiple document understanding datasets.
• Score: 32.51417315241559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Leveraging vast training data, multimodal large language models (MLLMs) have demonstrated formidable general visual comprehension capabilities and achieved remarkable performance across various tasks. However, their performance in visual document understanding still leaves much room for improvement. This discrepancy is primarily attributed to the fact that visual document understanding is a fine-grained prediction task. In natural scenes, MLLMs typically use low-resolution images, leading to a substantial loss of visual information. Furthermore, general-purpose MLLMs do not excel in handling document-oriented instructions. In this paper, we propose a High-Resolution Visual Document Assistant (HRVDA), which bridges the gap between MLLMs and visual document understanding. This model employs a content filtering mechanism and an instruction filtering module to separately filter out the content-agnostic visual tokens and instruction-agnostic visual tokens, thereby achieving efficient model training and inference for high-resolution images. In addition, we construct a document-oriented visual instruction tuning dataset and apply a multi-stage training strategy to enhance the model's document modeling capabilities. Extensive experiments demonstrate that our model achieves state-of-the-art performance across multiple document understanding datasets, while maintaining training efficiency and inference speed comparable to low-resolution models.

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