title: DynamicVis: An Efficient and General Visual Foundation Model for Remote
    Sensing Image Understanding
summary: The advancement of remote sensing technology has improved the spatial
  resolution of satellite imagery, facilitating more detailed visual
  representations for diverse interpretations. However, existing methods exhibit
  limited generalization capabilities across varied applications. While some
  contemporary foundation models demonstrate potential, they are hindered by
  insufficient cross-task adaptability and primarily process low-resolution
  imagery of restricted sizes, thus failing to fully exploit high-resolution data
  or leverage comprehensive large-scene semantics. Crucially, remote sensing
  imagery differs fundamentally from natural images, as key foreground targets
  (eg., maritime objects, artificial structures) often occupy minimal spatial
  proportions (~1%) and exhibit sparse distributions. Efficiently modeling
  cross-task generalizable knowledge from lengthy 2D tokens (~100,000) poses a
  significant challenge yet remains critical for remote sensing image
  understanding. Motivated by the selective attention mechanisms inherent to the
  human visual system, we propose DynamicVis, a dynamic visual perception
  foundation model for remote sensing imagery. The framework integrates a novel
  dynamic region perception backbone based on the selective state space model,
  which strategically balances localized detail extraction with global contextual
  integration, enabling computationally efficient encoding of large-scale data
  while maintaining architectural scalability. To enhance cross-task knowledge
  transferring, we introduce a multi-instance learning paradigm utilizing
  meta-embedding representations, trained on million-scale region-level
  annotations. Evaluations across nine downstream tasks demonstrate the model's
  versatility. DynamicVis achieves multi-level feature modeling with exceptional
  efficiency, processing (2048x2048) pixels with 97 ms latency (6% of ViT's) and
  833 MB GPU memory (3% of ViT's).
paper_authors._id: 67e2ac3b768537c7d7da2a88
paper_authors.name: Keyan Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T14:39:01.351Z
paper_authors._id: 62adcbf533c317b9b5dcf794
paper_authors.avatarUrl: /avatars/b4b8e2ecab25801b6b2ba4175073f605.svg
paper_authors.fullname: KeyanChen
paper_authors.type: user
paper_authors.user: KyanChen
paper_authors._id: 67e2ac3b768537c7d7da2a89
paper_authors.name: Chenyang Liu
paper_authors._id: 67e2ac3b768537c7d7da2a8a
paper_authors.name: Bowen Chen
paper_authors._id: 67e2ac3b768537c7d7da2a8b
paper_authors.name: Wenyuan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T14:40:00.722Z
paper_authors._id: 65029e5889707f182388f8d4
paper_authors.avatarUrl: /avatars/8d6f42be24266c548a8e9f4a93d2c854.svg
paper_authors.fullname: wenyuan Li
paper_authors.type: user
paper_authors.user: WenyuanLi
paper_authors._id: 67e2ac3b768537c7d7da2a8c
paper_authors.name: Zhengxia Zou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T14:39:36.651Z
paper_authors._id: 62cfc18780c9d4ceb2fedebf
paper_authors.avatarUrl: /avatars/a1cd4571cbda924d50eadd37c56079d9.svg
paper_authors.fullname: Zhengxia Zou
paper_authors.type: user
paper_authors.user: Jiupinjia
paper_authors._id: 67e2ac3b768537c7d7da2a8d
paper_authors.name: Zhenwei Shi
paper_title: DynamicVis: An Efficient and General Visual Foundation Model for Remote
    Sensing Image Understanding
paper_summary: The advancement of remote sensing technology has improved the spatial
  resolution of satellite imagery, facilitating more detailed visual
  representations for diverse interpretations. However, existing methods exhibit
  limited generalization capabilities across varied applications. While some
  contemporary foundation models demonstrate potential, they are hindered by
  insufficient cross-task adaptability and primarily process low-resolution
  imagery of restricted sizes, thus failing to fully exploit high-resolution data
  or leverage comprehensive large-scene semantics. Crucially, remote sensing
  imagery differs fundamentally from natural images, as key foreground targets
  (eg., maritime objects, artificial structures) often occupy minimal spatial
  proportions (~1%) and exhibit sparse distributions. Efficiently modeling
  cross-task generalizable knowledge from lengthy 2D tokens (~100,000) poses a
  significant challenge yet remains critical for remote sensing image
  understanding. Motivated by the selective attention mechanisms inherent to the
  human visual system, we propose DynamicVis, a dynamic visual perception
  foundation model for remote sensing imagery. The framework integrates a novel
  dynamic region perception backbone based on the selective state space model,
  which strategically balances localized detail extraction with global contextual
  integration, enabling computationally efficient encoding of large-scale data
  while maintaining architectural scalability. To enhance cross-task knowledge
  transferring, we introduce a multi-instance learning paradigm utilizing
  meta-embedding representations, trained on million-scale region-level
  annotations. Evaluations across nine downstream tasks demonstrate the model's
  versatility. DynamicVis achieves multi-level feature modeling with exceptional
  efficiency, processing (2048x2048) pixels with 97 ms latency (6% of ViT's) and
  833 MB GPU memory (3% of ViT's).
paper_ai_summary: DynamicVis, a dynamic visual perception foundation model for remote sensing, uses a selective state space model and multi-instance learning to efficiently handle high-resolution imagery and cross-task adaptability.
paper_ai_keywords: selective attention mechanisms dynamic region perception backbone selective state space model global contextual integration multi-instance learning meta-embedding representations feature modeling