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GraLoD, a plug-and-play framework that treats restoration scale as a spatially varying and stage-dependent continuous variable, is proposed and minimal-sufficient footprint calibration (MSFC) together with structure-aware regularization (SAR) is introduced to encourage restoration-effective and spatially coherent LOD assignments.
Existing image restoration models can now learn new tasks without sacrificing previously acquired skills, thanks to a novel capability-oriented routing mechanism.
GroupForward transforms 3D scene understanding by enabling complex referential reasoning, moving beyond basic semantic queries to nuanced interactions with 3D instances.