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Importance-Aware Sampling (IAS) reveals that not all patches in VIS-IR data are created equal, leading to substantial performance gains in multi-sensor perception tasks.
General-purpose computer vision MLLMs can outperform specialized remote sensing models on key tasks, challenging the notion of domain-specific superiority.
Fusing hyperspectral images just got sharper and more spectrally accurate: ASSR-Net leverages anisotropic structure awareness and spectral recalibration to outperform existing methods.
