Preprint
Aug 2026
SA-GEM: Scale-Adaptive and Geospatial Evidence-Modulated Token Pruning for Efficient Remote Sensing Large Vision-Language Models
It is shown that higher resolution is not universally beneficial and, once sufficient granularity is reached, token quality matters more than token quantity, and a plug-and-play framework that unifies task-adaptive token granularity allocation with holistic geospatial token importance modulation is presented.
Kexin Ma, Jing Xiao, Bowen Xing et al.
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