When Large Vision-Language Model Meets Large Remote Sensing Imagery:
  Coarse-to-Fine Text-Guided Token Pruning
Efficient vision-language understanding of large Remote Sensing Images (RSIs)
is meaningful but challenging. Current Large Vision-Language Models (LVLMs)
typically employ limited pre-defined grids to process images, leading to
information loss when handling gigapixel RSIs. Conversely, using unlimited
grids significantly increases computational costs. To preserve image details
while reducing computational complexity, we propose a text-guided token pruning
method with Dynamic Image Pyramid (DIP) integration. Our method introduces: (i)
a Region Focus Module (RFM) that leverages text-aware region localization
capability to identify critical vision tokens, and (ii) a coarse-to-fine image
tile selection and vision token pruning strategy based on DIP, which is guided
by RFM outputs and avoids directly processing the entire large imagery.
Additionally, existing benchmarks for evaluating LVLMs' perception ability on
large RSI suffer from limited question diversity and constrained image sizes.
We construct a new benchmark named LRS-VQA, which contains 7,333 QA pairs
across 8 categories, with image length up to 27,328 pixels. Our method
outperforms existing high-resolution strategies on four datasets using the same
data. Moreover, compared to existing token reduction methods, our approach
demonstrates higher efficiency under high-resolution settings. Dataset and code
are in https://github.com/VisionXLab/LRS-VQA.
67d1808e13d7b3f8c6ea9147
Junwei Luo
67d1808e13d7b3f8c6ea9148
Yingying Zhang
67d1808e13d7b3f8c6ea9149
Xue Yang
claimed_verified
2025-04-04T07:10:03.567Z
648e77184cae4f6921dbb382
https://cdn-avatars.huggingface.co/v1/production/uploads/648e77184cae4f6921dbb382/zAAJRvOStC0wZplqVWrk_.jpeg
Xue Yang
user
yangxue
67d1808e13d7b3f8c6ea914a
Kang Wu
67d1808e13d7b3f8c6ea914b
Qi Zhu
67d1808e13d7b3f8c6ea914c
Lei Liang
67d1808e13d7b3f8c6ea914d
Jingdong Chen
67d1808e13d7b3f8c6ea914e
Yansheng Li
When Large Vision-Language Model Meets Large Remote Sensing Imagery:
  Coarse-to-Fine Text-Guided Token Pruning
Efficient vision-language understanding of large Remote Sensing Images (RSIs)
is meaningful but challenging. Current Large Vision-Language Models (LVLMs)
typically employ limited pre-defined grids to process images, leading to
information loss when handling gigapixel RSIs. Conversely, using unlimited
grids significantly increases computational costs. To preserve image details
while reducing computational complexity, we propose a text-guided token pruning
method with Dynamic Image Pyramid (DIP) integration. Our method introduces: (i)
a Region Focus Module (RFM) that leverages text-aware region localization
capability to identify critical vision tokens, and (ii) a coarse-to-fine image
tile selection and vision token pruning strategy based on DIP, which is guided
by RFM outputs and avoids directly processing the entire large imagery.
Additionally, existing benchmarks for evaluating LVLMs' perception ability on
large RSI suffer from limited question diversity and constrained image sizes.
We construct a new benchmark named LRS-VQA, which contains 7,333 QA pairs
across 8 categories, with image length up to 27,328 pixels. Our method
outperforms existing high-resolution strategies on four datasets using the same
data. Moreover, compared to existing token reduction methods, our approach
demonstrates higher efficiency under high-resolution settings. Dataset and code
are in https://github.com/VisionXLab/LRS-VQA.
A text-guided token pruning method with Dynamic Image Pyramid integration improves vision-language understanding for large Remote Sensing Images by focusing on critical regions and reducing computational cost.
Large Vision-Language Models LVLMs Remote Sensing Images RSIs text-guided token pruning Dynamic Image Pyramid DIP Region Focus Module RFM coarse-to-fine image tile selection vision token pruning LRS-VQA