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A change-aware remote sensing STF method via frequency decoupling and symmetric gating, termed CA-STF, which achieves superior fusion accuracy and visual quality in complex spatiotemporal dynamics.
AdaDINO is presented, a pair-aware in-backbone adaptation framework that equips a frozen DINO encoder with bi-temporal interaction for efficient change detection and achieves competitive or superior performance against VFM-based baselines.
DARAD is proposed, a dual-adapter and ranking-aware distillation framework that preserves the historical cross-modal ranking structure while learning new visual and textual concepts from evolving archives to address the challenge of continual RS-ITR.
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