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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.11297 |
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| _version_ | 1866912895368953856 |
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| author | Du, Yuhao Liu, Hui Peng, Haoxiang Cheng, Xinyuan Wu, Chengrong Zhang, Jiankai |
| author_facet | Du, Yuhao Liu, Hui Peng, Haoxiang Cheng, Xinyuan Wu, Chengrong Zhang, Jiankai |
| contents | Recent years, weather forecasting has gained significant attention. However, accurately predicting weather remains a challenge due to the rapid variability of meteorological data and potential teleconnections. Current spatiotemporal forecasting models primarily rely on convolution operations or sliding windows for feature extraction. These methods are limited by the size of the convolutional kernel or sliding window, making it difficult to capture and identify potential teleconnection features in meteorological data. Additionally, weather data often involve non-rigid bodies, whose motion processes are accompanied by unpredictable deformations, further complicating the forecasting task. In this paper, we propose the GMG model to address these two core challenges. The Global Focus Module, a key component of our model, enhances the global receptive field, while the Motion Guided Module adapts to the growth or dissipation processes of non-rigid bodies. Through extensive evaluations, our method demonstrates competitive performance across various complex tasks, providing a novel approach to improving the predictive accuracy of complex spatiotemporal data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11297 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GMG: A Video Prediction Method Based on Global Focus and Motion Guided Du, Yuhao Liu, Hui Peng, Haoxiang Cheng, Xinyuan Wu, Chengrong Zhang, Jiankai Computer Vision and Pattern Recognition Recent years, weather forecasting has gained significant attention. However, accurately predicting weather remains a challenge due to the rapid variability of meteorological data and potential teleconnections. Current spatiotemporal forecasting models primarily rely on convolution operations or sliding windows for feature extraction. These methods are limited by the size of the convolutional kernel or sliding window, making it difficult to capture and identify potential teleconnection features in meteorological data. Additionally, weather data often involve non-rigid bodies, whose motion processes are accompanied by unpredictable deformations, further complicating the forecasting task. In this paper, we propose the GMG model to address these two core challenges. The Global Focus Module, a key component of our model, enhances the global receptive field, while the Motion Guided Module adapts to the growth or dissipation processes of non-rigid bodies. Through extensive evaluations, our method demonstrates competitive performance across various complex tasks, providing a novel approach to improving the predictive accuracy of complex spatiotemporal data. |
| title | GMG: A Video Prediction Method Based on Global Focus and Motion Guided |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.11297 |