Summary of Texthawk2: a Large Vision-language Model Excels in Bilingual Ocr and Grounding with 16x Fewer Tokens, by Ya-qi Yu et al.
TextHawk2: A Large Vision-Language Model Excels in Bilingual OCR and Grounding with 16x Fewer Tokens
by Ya-Qi Yu, Minghui Liao, Jiwen Zhang, Jihao Wu
First submitted to arxiv on: 7 Oct 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 presents TextHawk2, a Large Vision-Language Model (LVLM) that excels in both reading dense text and locating objects within images. Unlike previous LVLMs like GPT-4o, which struggled to perform well in both tasks simultaneously, TextHawk2 achieves cutting-edge performance across general-purpose, OCR, and grounding tasks using only 16 times fewer image tokens than its predecessors. The model’s efficiency is attributed to three key improvements: Token Compression, Visual Encoder Reinforcement, and Data Diversity. Specifically, the paper introduces a novel architecture that reduces the number of tokens per image by 16 times, making it possible to train and deploy TextHawk2 with minimal resources. Additionally, the authors enhance the visual encoder through LVLM co-training, unlocking its potential for previously unseen tasks like Chinese OCR and grounding. The paper assesses TextHawk2 across multiple benchmarks, demonstrating superior performance and outperforming closed-source models of similar scale. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new kind of artificial intelligence called TextHawk2 that can read text and find objects in images really well. It’s better than other AI models at doing both tasks at the same time. The authors made some changes to make it more efficient, which means it uses fewer computer resources. They also trained the model on a wider variety of data, which helps it learn new things. The paper shows that TextHawk2 is really good at recognizing text and finding objects in images, and it even outperforms other AI models that are similar but not open-source. |
Keywords
» Artificial intelligence » Encoder » Gpt » Grounding » Language model » Token
