Efficient Remote Sensing Instance Segmentation with Linear-Time State Space Distilled Visual Foundation Models

• URL: http://arxiv.org/abs/2606.25324v1
• Date: Wed, 24 Jun 2026 02:41:23 GMT
• Title: Efficient Remote Sensing Instance Segmentation with Linear-Time State Space Distilled Visual Foundation Models
• Abstract summary: We introduce RS4D - a new remote sensing instance segmentation method with linear computational complexity.<n>We propose an adaptive noise and masking knowledge distillation training method for pre-training lightweight SSM backbones.<n>Compared to ViT-based approaches, our proposed SSM backbone achieves an 8x reduction in parameters and a 9x reduction in FLOPs.
• Score: 32.823741317677744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The computational complexity of Transformers scales quadratically with the number of tokens, which significantly constrains the efficiency of vision models, particularly recent ViT-based foundation models in dense prediction tasks. Instance segmentation, a typical dense visual prediction task in the remote sensing field, faces similar challenges. In this paper, inspired by the recent advances of knowledge distillation in large language models, we introduce RS4D - a new remote sensing instance segmentation method with linear computational complexity, which addresses the inefficiency of long sequence modeling through distilled state space modeling (SSM). We propose an adaptive noise and masking knowledge distillation training method for pre-training lightweight SSM backbones, which effectively compresses knowledge from the vast self-attention space into a compact, dense linear state space. We also design a remote sensing image instance segmentation architecture based on this lightweight visual encoder, where we explore variants of three different backbones and two segmentation heads. Extensive experiments are conducted on multiple benchmark datasets, including SSDD, WHU, and NWPU. Compared to ViT-based approaches, our proposed SSM backbone achieves an 8x reduction in parameters and a 9x reduction in FLOPs while maintaining comparable or superior accuracy to both ViT- and CNN-based instance segmentation methods. The implementation codes have been publicly available at https://github.com/QinzheYang/RS4D.

Related papers

• GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence [50.10864740041483]
  We propose a training-free 3D instance segmentation approach via Geometric Visual Correspondence (GVC-Seg)<n>GVC-Seg exploits the correspondence between 3D geometric cues and 2D visual cues to mitigate the confidence bias.<n>Our method achieves state-of-the-art performance on several challenging benchmarks, while also exhibiting strong potential in open-vocabulary semantic segmentation settings.
  arXiv  Detail & Related papers  (2026-06-06T07:09:15Z)
• Few-Shot Video Object Segmentation in X-Ray Angiography Using Local Matching and Spatio-Temporal Consistency Loss [13.850743997507488]
  We introduce a novel FSVOS model that employs a local matching strategy to restrict the search space to the most neighboring pixels.<n>Specifically, we implement non-parametric sampling mechanism that enables dynamically varying sampling regions.<n>This work offers enhanced potential for a wide range of clinical applications.
  arXiv  Detail & Related papers  (2026-01-02T21:26:28Z)
• RangeSAM: Leveraging Visual Foundation Models for Range-View repesented LiDAR segmentation [6.513648249086729]
  We present the first range-view framework that adapts SAM2 to 3D segmentation, coupling efficient 2D feature extraction with standard projection/back-projection to operate on point clouds.<n>Our approach achieves competitive performance on Semantic KITTI while benefiting from the speed, scalability, and deployment simplicity of 2D-centric pipelines.
  arXiv  Detail & Related papers  (2025-09-19T11:33:10Z)
• Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing [55.291219073365546]
  Open-Vocabulary Remote Sensing Image (OVRSIS) is an emerging task that adapts Open-Vocabulary (OVS) to the remote sensing (RS) domain.<n>textbfRSKT-Seg is a novel open-vocabulary segmentation framework tailored for remote sensing.<n> RSKT-Seg consistently outperforms strong OVS baselines by +3.8 mIoU and +5.9 mACC, while achieving 2x faster inference through efficient aggregation.
  arXiv  Detail & Related papers  (2025-09-15T15:24:49Z)
• Geometric Operator Learning with Optimal Transport [77.16909146519227]
  We propose integrating optimal transport (OT) into operator learning for partial differential equations (PDEs) on complex geometries.<n>For 3D simulations focused on surfaces, our OT-based neural operator embeds the surface geometry into a 2D parameterized latent space.<n> Experiments with Reynolds-averaged Navier-Stokes equations (RANS) on the ShapeNet-Car and DrivAerNet-Car datasets show that our method achieves better accuracy and also reduces computational expenses.
  arXiv  Detail & Related papers  (2025-07-26T21:28:25Z)
• Hi^2-GSLoc: Dual-Hierarchical Gaussian-Specific Visual Relocalization for Remote Sensing [6.997091164331322]
  Visual relocalization is fundamental to remote sensing and UAV applications.<n>Existing methods face inherent trade-offs: image-based retrieval and pose regression approaches lack precision.<n>We introduce $mathrmHi2$-GSLoc, a dual-hierarchical relocalization framework that follows a sparse-to-dense and coarse-to-fine paradigm.
  arXiv  Detail & Related papers  (2025-07-21T14:47:56Z)
• Efficient High-Resolution Visual Representation Learning with State Space Model for Human Pose Estimation [60.80423207808076]
  Capturing long-range dependencies while preserving high-resolution visual representations is crucial for dense prediction tasks such as human pose estimation.<n>We propose the Dynamic Visual State Space (DVSS) block, which augments visual state space models with multi-scale convolutional operations.<n>We build HRVMamba, a novel model for efficient high-resolution representation learning.
  arXiv  Detail & Related papers  (2024-10-04T06:19:29Z)
• Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling [52.464516118826765]
  We introduce RandLA-Net, an efficient and lightweight neural architecture to infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Our RandLA-Net can process 1 million points in a single pass up to 200x faster than existing approaches.
  arXiv  Detail & Related papers  (2021-07-06T05:08:34Z)
• Unsupervised Learning Consensus Model for Dynamic Texture Videos Segmentation [12.462608802359936]
  We present an effective unsupervised learning consensus model for the segmentation of dynamic texture (ULCM) In the proposed model, the set of values of the requantized local binary patterns (LBP) histogram around the pixel to be classified are used as features. Experiments conducted on the challenging SynthDB dataset show that ULCM is significantly faster, easier to code, simple and has limited parameters.
  arXiv  Detail & Related papers  (2020-06-29T16:40:59Z)
• Spatial Pyramid Based Graph Reasoning for Semantic Segmentation [67.47159595239798]
  We apply graph convolution into the semantic segmentation task and propose an improved Laplacian. The graph reasoning is directly performed in the original feature space organized as a spatial pyramid. We achieve comparable performance with advantages in computational and memory overhead.
  arXiv  Detail & Related papers  (2020-03-23T12:28:07Z)
• On the Texture Bias for Few-Shot CNN Segmentation [21.349705243254423]
  Convolutional Neural Networks (CNNs) are driven by shapes to perform visual recognition tasks. Recent evidence suggests texture bias in CNNs provides higher performing models when learning on large labeled training datasets. We propose a novel architecture that integrates a set of Difference of Gaussians (DoG) to attenuate high-frequency local components in the feature space.
  arXiv  Detail & Related papers  (2020-03-09T11:55:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.