uzh-rpg/gg_ssms
[CVPR'25 Highlight] The official implementation of "GG-SSMs: Graph-Generating State Space Models"
This project helps computer vision and data scientists analyze complex, high-dimensional data like images, videos, or time series. It takes in raw sequential data and processes it by dynamically building relationships between features, outputting highly accurate classifications, forecasts, or estimations. Researchers and practitioners working with sensor data, medical imaging, or financial time series would find this useful.
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Use this if you need to extract meaningful patterns and make predictions from complex, high-dimensional sequential data where traditional methods struggle to capture intricate relationships.
Not ideal if your data is simple, low-dimensional, or static, as the overhead of dynamic graph generation might not be necessary.
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Python
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Jun 05, 2025
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