title: When Large Vision-Language Model Meets Large Remote Sensing Imagery:
    Coarse-to-Fine Text-Guided Token Pruning
summary: 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.
paper_authors._id: 67d1808e13d7b3f8c6ea9147
paper_authors.name: Junwei Luo
paper_authors._id: 67d1808e13d7b3f8c6ea9148
paper_authors.name: Yingying Zhang
paper_authors._id: 67d1808e13d7b3f8c6ea9149
paper_authors.name: Xue Yang
paper_authors._id: 67d1808e13d7b3f8c6ea914a
paper_authors.name: Kang Wu
paper_authors._id: 67d1808e13d7b3f8c6ea914b
paper_authors.name: Qi Zhu
paper_authors._id: 67d1808e13d7b3f8c6ea914c
paper_authors.name: Lei Liang
paper_authors._id: 67d1808e13d7b3f8c6ea914d
paper_authors.name: Jingdong Chen
paper_authors._id: 67d1808e13d7b3f8c6ea914e
paper_authors.name: Yansheng Li
paper_title: When Large Vision-Language Model Meets Large Remote Sensing Imagery:
    Coarse-to-Fine Text-Guided Token Pruning
paper_summary: 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.
paper_ai_keywords: Large Vision-Language Models (LVLMs) gigapixel RSIs Dynamic Image Pyramid (DIP) text-guided token pruning Region Focus Module (RFM) text-aware region localization vision tokens coarse-to-fine image tile selection vision token pruning strategy large imagery LRS-VQA QA pairs high-resolution strategies token reduction methods