Summary of Trins: Towards Multimodal Language Models That Can Read, by Ruiyi Zhang et al.
TRINS: Towards Multimodal Language Models that Can Read
by Ruiyi Zhang, Yanzhe Zhang, Jian Chen, Yufan Zhou, Jiuxiang Gu, Changyou Chen, Tong Sun
First submitted to arxiv on: 10 Jun 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 A new multimodal language model is introduced to improve the reading ability of visually-tuned models. The paper focuses on enhancing the comprehension of textual content embedded in images by creating a Text-Rich image INStruction (TRINS) dataset. TRINS contains 39,153 text-rich images, captions, and 102,437 questions, with longer annotation lengths compared to related datasets, providing new challenges for multimodal large language models. The proposed Language-vision Reading Assistant (LaRA) architecture outperforms existing state-of-the-art models on the TRINS dataset and classical benchmarks. The paper also presents a comprehensive evaluation of TRINS on various text-rich image understanding and generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers understand images with words is introduced in this research. Right now, some computer programs are very good at looking at pictures and recognizing what’s inside them. But they often struggle to read the words that might be included in those pictures. To fix this, the researchers created a big dataset of images, captions, and questions (called TRINS) that can help train these computer programs to do better. They also developed a new way for computers to understand text in images, called LaRA. This approach does better than other existing ways on some important tasks, like recognizing words in pictures. |
Keywords
» Artificial intelligence » Language model
