DynamicVis: An Efficient and General Visual Foundation Model for Remote
  Sensing Image Understanding
Authors: name: Keyan Chen; name: Chenyang Liu; name: Bowen Chen; name: Wenyuan Li; name: Zhengxia Zou; name: Zhenwei Shi
Published: 2025-03-20 17:59:54

Abstract: 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).