VMU-Diff improves precipitation nowcasting via coarse multi-source Vision Mamba fusion followed by residual conditional diffusion refinement.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
TA-SmaAt-UNet extends SmaAt-UNet with temporal conditioning via cyclical time encodings, showing benefits for high-intensity precipitation nowcasting on KNMI radar data.
citing papers explorer
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VMU-Diff: A Coarse-to-fine Multi-source Data Fusion Framework for Precipitation Nowcasting
VMU-Diff improves precipitation nowcasting via coarse multi-source Vision Mamba fusion followed by residual conditional diffusion refinement.
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Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall
TA-SmaAt-UNet extends SmaAt-UNet with temporal conditioning via cyclical time encodings, showing benefits for high-intensity precipitation nowcasting on KNMI radar data.