DynamicVis: An Efficient and General Visual Foundation Model for Remote
  Sensing Image Understanding
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).
67e2ac3b768537c7d7da2a88
Keyan Chen
admin_assigned
2025-03-25T14:39:01.351Z
62adcbf533c317b9b5dcf794
/avatars/b4b8e2ecab25801b6b2ba4175073f605.svg
KeyanChen
user
KyanChen
67e2ac3b768537c7d7da2a89
Chenyang Liu
67e2ac3b768537c7d7da2a8a
Bowen Chen
67e2ac3b768537c7d7da2a8b
Wenyuan Li
admin_assigned
2025-03-25T14:40:00.722Z
65029e5889707f182388f8d4
/avatars/8d6f42be24266c548a8e9f4a93d2c854.svg
wenyuan Li
user
WenyuanLi
67e2ac3b768537c7d7da2a8c
Zhengxia Zou
admin_assigned
2025-03-25T14:39:36.651Z
62cfc18780c9d4ceb2fedebf
/avatars/a1cd4571cbda924d50eadd37c56079d9.svg
Zhengxia Zou
user
Jiupinjia
67e2ac3b768537c7d7da2a8d
Zhenwei Shi
DynamicVis: An Efficient and General Visual Foundation Model for Remote
  Sensing Image Understanding
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).
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.
selective attention mechanisms dynamic region perception backbone selective state space model global contextual integration multi-instance learning meta-embedding representations feature modeling