5 papers
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…
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…
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…
SGMA: Semantic-Guided Modality-Aware Segmentation for Remote Sensing with Incomplete Multimodal Data
Lekang Wen, Liang Liao, Jing Xiao +1
Multimodal semantic segmentation integrates complementary information from diverse sensors for remote sensing Earth observation. However, practical systems often encounter missing…
DAWA: Dynamic Ambiguity-Wise Adaptation for Real-Time Domain Adaptive Semantic Segmentation
Taorong Liu, Zhen Zhang, Liang Liao +2
Test-time domain adaption (TTDA) for semantic segmentation aims to adapt a segmentation model trained on a source domain to a target domain for inference on-the-fly, where both eff…