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cs.CV2026
SA-GEM: Scale-Adaptive and Geospatial Evidence-Modulated Token Pruning for Efficient Remote Sensing Large Vision-Language Models
Kexin Ma, Jing Xiao, Bowen Xing +2
RS-LVLMs have advanced multimodal understanding of Earth observation imagery, yet their performance is fundamentally constrained by high-resolution processing, as visual token coun…
cs.CV2026
Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding
Zhenghao Xie, Jing Xiao, Zhenqi Wang +4
Remote sensing understanding inherently requires multi-resolution observation, since different targets and application tasks demand different levels of spatial detail. While low-re…
cs.CV2026
Decoupled Similarity for Task-Aware Token Pruning in Large Vision-Language Models
Kexin Ma, Jing Xiao, Chaofeng Chen +4
Token pruning has emerged as an effective approach to reduce the substantial computational overhead of Large Vision-Language Models (LVLMs) by discarding less informative visual to…