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Hunan University, Yuelushan Center for Industrial Innovation
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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.
Training remote sensing image-text retrieval models on real-world noisy data can be significantly improved by a self-paced learning strategy that mimics human cognitive learning patterns.
