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Universit脿 Politecnica delle Marche
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High-fidelity captions generated for disaster response images reveal a surprising 78.65 semantic agreement, exposing inconsistencies in human annotations.
Learnable filter shapes in RF-KAN yield a performance boost that outstrips conventional convolutional approaches while slashing parameter counts by over 80%.
