Summary of Llava-uhd: An Lmm Perceiving Any Aspect Ratio and High-resolution Images, by Ruyi Xu et al.
LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images
by Ruyi Xu, Yuan Yao, Zonghao Guo, Junbo Cui, Zanlin Ni, Chunjiang Ge, Tat-Seng Chua, Zhiyuan Liu, Maosong Sun, Gao Huang
First submitted to arxiv on: 18 Mar 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 addresses the limitations of large multimodal models (LMMs) in processing visual data. Conventional LMMs are restricted to fixed sizes and resolutions, whereas recent efforts have been hindered by lack of adaptivity, efficiency, and correctness. The authors expose systematic flaws in existing visual encoding strategies using GPT-4V and LLaVA-1.5 as examples. To overcome these challenges, they introduce LLaVA-UHD, a large multimodal model that can efficiently process images in any aspect ratio and high resolution. This model consists of three key components: image modularization, compression, and spatial schema. Experimental results demonstrate that LLaVA-UHD outperforms established LMMs trained with significantly more data on nine benchmarks. Notably, the model achieves 6.4 accuracy improvement on TextVQA and can be efficiently trained using 8 A100 GPUs in under 23 hours. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about fixing a problem with big computer models that understand pictures. These models are limited because they don’t work well with images that are different sizes or resolutions. The authors show that these limitations come from how the models process visual information. To fix this, they create a new model called LLaVA-UHD that can handle any size or resolution of image efficiently and accurately. This new model has three main parts: breaking down big images into smaller pieces, compressing the information in those pieces, and organizing it all together. The results show that LLaVA-UHD is better than other models at understanding pictures, and it can be trained using less powerful computers. |
Keywords
» Artificial intelligence » Gpt
