Papers › All in an Aggregated Image for In-Image Learning
All in an Aggregated Image for In-Image Learning
Lei Wang, Wanyu Xu, Zhiqiang Hu, Yihuai Lan, Shan Dong, Hao Wang, Roy Ka-Wei Lee, Ee-Peng Lim
This paper introduces a new in-context learning (ICL) mechanism called In-Image Learning (I²L) that combines demonstration examples, visual cues, and chain-of-thought reasoning into an aggregated image to enhance the capabilities of Large Multimodal Models (e.g., GPT-4V) in multimodal reasoning tasks. Unlike previous approaches that rely on converting images to text or incorporating visual input into language models, I²L consolidates all information into an aggregated image and leverages image processing, understanding, and reasoning abilities. This has several advantages: it reduces inaccurate textual descriptions of complex images, provides flexibility in positioning demonstration examples, and avoids multiple input images and lengthy prompts. We also introduce I²L-Hybrid, a method that combines the strengths of I²L with other ICL methods. Specifically, it uses an automatic strategy to select the most suitable method (I²L or another certain ICL method) for a specific task instance. We conduct extensive experiments to assess the effectiveness of I²L and I²L-Hybrid on MathVista, which covers a variety of complex multimodal reasoning tasks. Additionally, we investigate the influence of image resolution, the number of demonstration examples in a single image, and the positions of these demonstrations in the aggregated image on the effectiveness of I²L. Our code is publicly available at https://github.com/AGI-Edgerunners/IIL.
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