Summary of All in An Aggregated Image For In-image Learning, by Lei Wang et al.
All in an Aggregated Image for In-Image Learning
by Lei Wang, Wanyu Xu, Zhiqiang Hu, Yihuai Lan, Shan Dong, Hao Wang, Roy Ka-Wei Lee, Ee-Peng Lim
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 a novel in-context learning (ICL) mechanism called In-Image Learning (I^2L), which combines demonstration examples, visual cues, and chain-of-thought reasoning to enhance the capabilities of Large Multimodal Models (e.g., GPT-4V) in multimodal reasoning tasks. Unlike previous approaches, I^2L consolidates information into an aggregated image, leveraging image processing, understanding, and reasoning abilities. This reduces inaccurate textual descriptions of complex images, provides flexibility in positioning demonstration examples, and avoids lengthy prompts. The authors also introduce I^2L-Hybrid, a method combining the strengths of I^2L with other ICL methods. Extensive experiments on MathVista, a dataset covering various complex multimodal reasoning tasks, demonstrate the effectiveness of I^2L and I^2L-Hybrid. The influence of image resolution, number of demonstration examples, and their positions on the aggregated image is also investigated. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to help computers learn from images called In-Image Learning (I^2L). It’s different from other methods because it puts all the information into one image instead of converting it to text or combining multiple images. This makes it better at understanding complex images and reduces mistakes in describing them. The authors also created a new method that combines I^2L with other ways of learning from images. They tested these methods on a dataset called MathVista, which has many different problems that require multimodal reasoning. The results show that their methods are effective. |
Keywords
» Artificial intelligence » Gpt
