TextHawk: Exploring Efficient Fine-Grained Perception of Multimodal
  Large Language Models
Ya-Qi Yu Minghui Liao Jihao Wu Yongxin Liao Xiaoyu Zheng Wei Zeng
Multimodal Large Language Models (MLLMs) have shown impressive results on
various multimodal tasks. However, most existing MLLMs are not well suited for
document-oriented tasks, which require fine-grained image perception and
information compression. In this paper, we present TextHawk, a MLLM that is
specifically designed for document-oriented tasks, while preserving the general
capabilities of MLLMs. TextHawk is aimed to explore efficient fine-grained
perception by designing four dedicated components. Firstly, a ReSampling and
ReArrangement (ReSA) module is proposed to reduce the redundancy in the
document texts and lower the computational cost of the MLLM. We explore
encoding the positions of each local feature by presenting Scalable Positional
Embeddings (SPEs), which can preserve the scalability of various image sizes. A
Query Proposal Network (QPN) is then adopted to initialize the queries
dynamically among different sub-images. To further enhance the fine-grained
visual perceptual ability of the MLLM, we design a Multi-Level Cross-Attention
(MLCA) mechanism that captures the hierarchical structure and semantic
relations of document images. Furthermore, we create a new instruction-tuning
dataset for document-oriented tasks by enriching the multimodal document data
with Gemini Pro. We conduct extensive experiments on both general and
document-oriented MLLM benchmarks, and show that TextHawk outperforms the
state-of-the-art methods, demonstrating its effectiveness and superiority in
fine-grained document perception and general abilities.