Summary of Texthawk: Exploring Efficient Fine-grained Perception Of Multimodal Large Language Models, by Ya-qi Yu et al.
TextHawk: Exploring Efficient Fine-Grained Perception of Multimodal Large Language Models
by Ya-Qi Yu, Minghui Liao, Jihao Wu, Yongxin Liao, Xiaoyu Zheng, Wei Zeng
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces TextHawk, a Multimodal Large Language Model (MLLM) designed for document-oriented tasks. Unlike existing MLLMs, TextHawk is optimized for fine-grained image perception and information compression. The model consists of four dedicated components: ReSampling and ReArrangement (ReSA), Scalable Positional Embeddings (SPEs), Query Proposal Network (QPN), and Multi-Level Cross-Attention (MLCA). These components enable TextHawk to efficiently perceive document images, capture hierarchical structures, and learn semantic relations. The authors create a new dataset, Gemini Pro, to train and evaluate the model on document-oriented tasks. Experimental results show that TextHawk outperforms state-of-the-art methods on both general and document-oriented MLLM benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a special type of artificial intelligence called TextHawk. It’s designed to understand documents better than other similar models. The team created four new parts for the model that help it look closely at images, learn about the relationships between different parts of an image, and reduce unnecessary information. They also made a new dataset with more examples of documents and used it to test TextHawk. The results show that TextHawk is better than other models at understanding documents. |
Keywords
» Artificial intelligence » Cross attention » Gemini » Large language model
