Summary of Weathergfm: Learning a Weather Generalist Foundation Model Via In-context Learning, by Xiangyu Zhao et al.
WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning
by Xiangyu Zhao, Zhiwang Zhou, Wenlong Zhang, Yihao Liu, Xiangyu Chen, Junchao Gong, Hao Chen, Ben Fei, Shiqi Chen, Wanli Ouyang, Xiao-Ming Wu, Lei Bai
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Atmospheric and Oceanic Physics (physics.ao-ph)
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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 The paper introduces a novel approach to addressing various complex weather understanding tasks using a unified model. It draws inspiration from visual foundation models and large language models to develop the first generalist weather foundation model (WeatherGFM). The WeatherGFM is designed to tackle a wide range of weather understanding tasks, including weather forecasting, super-resolution, weather image translation, and post-processing. To achieve this, the paper unifies the representation and definition of diverse weather understanding tasks, devises prompt formats for managing different weather data modalities, and adopts a visual prompting question-answering paradigm for training. The proposed method is evaluated through extensive experiments, demonstrating its effectiveness in handling up to ten weather understanding tasks and showcasing generalization ability on unseen tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to understand the weather by using a single model that can do many things. It’s like a super-smart computer that can answer lots of different questions about the weather. The model is called WeatherGFM, and it was inspired by other AI models that are good at understanding pictures and text. To make the WeatherGFM work, the researchers had to come up with a way to define all the different things they want the model to understand, like what the weather will be tomorrow or what an image of the sun looks like. They also came up with special prompts to help the model learn from different types of weather data. The results show that WeatherGFM can do many tasks well and even learn new ones it hasn’t seen before. |
Keywords
» Artificial intelligence » Generalization » Prompt » Prompting » Question answering » Super resolution » Translation
