DefMamba: Deformable Visual State Space Model

• URL: http://arxiv.org/abs/2504.05794v1
• Date: Tue, 08 Apr 2025 08:22:54 GMT
• Title: DefMamba: Deformable Visual State Space Model
• Authors: Leiye Liu, Miao Zhang, Jihao Yin, Tingwei Liu, Wei Ji, Yongri Piao, Huchuan Lu,
• Abstract summary: We propose a novel visual foundation model called DefMamba.<n>By combining a deformable scanning(DS) strategy, this model significantly improves its ability to learn image structures and detects changes in object details.<n>Numerous experiments have shown that DefMamba achieves state-of-the-art performance in various visual tasks.
• Score: 65.50381013020248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, state space models (SSM), particularly Mamba, have attracted significant attention from scholars due to their ability to effectively balance computational efficiency and performance. However, most existing visual Mamba methods flatten images into 1D sequences using predefined scan orders, which results the model being less capable of utilizing the spatial structural information of the image during the feature extraction process. To address this issue, we proposed a novel visual foundation model called DefMamba. This model includes a multi-scale backbone structure and deformable mamba (DM) blocks, which dynamically adjust the scanning path to prioritize important information, thus enhancing the capture and processing of relevant input features. By combining a deformable scanning(DS) strategy, this model significantly improves its ability to learn image structures and detects changes in object details. Numerous experiments have shown that DefMamba achieves state-of-the-art performance in various visual tasks, including image classification, object detection, instance segmentation, and semantic segmentation. The code is open source on DefMamba.

Related papers

• DAMamba: Vision State Space Model with Dynamic Adaptive Scan [51.81060691414399]
  State space models (SSMs) have recently garnered significant attention in computer vision.<n>We propose Dynamic Adaptive Scan (DAS), a data-driven method that adaptively allocates scanning orders and regions.<n>Based on DAS, we propose the vision backbone DAMamba, which significantly outperforms current state-of-the-art vision Mamba models in vision tasks.
  arXiv  Detail & Related papers  (2025-02-18T08:12:47Z)
• Detail Matters: Mamba-Inspired Joint Unfolding Network for Snapshot Spectral Compressive Imaging [40.80197280147993]
  We propose a Mamba-inspired Joint Unfolding Network (MiJUN) to overcome the inherent nonlinear and ill-posed characteristics of HSI reconstruction. We introduce an accelerated unfolding network scheme, which reduces the reliance on initial optimization stages. We refine the scanning strategy with Mamba by integrating the tensor mode-$k$ unfolding into the Mamba network.
  arXiv  Detail & Related papers  (2025-01-02T13:56:23Z)
• V2M: Visual 2-Dimensional Mamba for Image Representation Learning [68.51380287151927]
  Mamba has garnered widespread attention due to its flexible design and efficient hardware performance to process 1D sequences. Recent studies have attempted to apply Mamba to the visual domain by flattening 2D images into patches and then regarding them as a 1D sequence. We propose a Visual 2-Dimensional Mamba model as a complete solution, which directly processes image tokens in the 2D space.
  arXiv  Detail & Related papers  (2024-10-14T11:11:06Z)
• StableMamba: Distillation-free Scaling of Large SSMs for Images and Videos [27.604572990625144]
  State-space models (SSMs) have introduced a novel context modeling method by integrating state-space techniques into deep learning.<n>Mamba-based architectures are difficult to scale with respect to the number of parameters, which is a major limitation for vision applications.<n>We propose a Mamba-Attention interleaved architecture that enhances scalability, robustness, and performance.
  arXiv  Detail & Related papers  (2024-09-18T10:48:10Z)
• Cross-Scan Mamba with Masked Training for Robust Spectral Imaging [51.557804095896174]
  We propose the Cross-Scanning Mamba, named CS-Mamba, that employs a Spatial-Spectral SSM for global-local balanced context encoding. Experiment results show that our CS-Mamba achieves state-of-the-art performance and the masked training method can better reconstruct smooth features to improve the visual quality.
  arXiv  Detail & Related papers  (2024-08-01T15:14:10Z)
• MambaVision: A Hybrid Mamba-Transformer Vision Backbone [54.965143338206644]
  We propose a novel hybrid Mamba-Transformer backbone, MambaVision, specifically tailored for vision applications.<n>We show that equipping the Mamba architecture with self-attention blocks in the final layers greatly improves its capacity to capture long-range spatial dependencies.<n>For classification on the ImageNet-1K dataset, MambaVision variants achieve state-of-the-art (SOTA) performance in terms of both Top-1 accuracy and throughput.
  arXiv  Detail & Related papers  (2024-07-10T23:02:45Z)
• A Novel State Space Model with Local Enhancement and State Sharing for Image Fusion [14.293042131263924]
  In image fusion tasks, images from different sources possess distinct characteristics. Mamba, as a state space model, has emerged in the field of natural language processing. Motivated by these challenges, we customize and improve the vision Mamba network designed for the image fusion task.
  arXiv  Detail & Related papers  (2024-04-14T16:09:33Z)
• RSMamba: Remote Sensing Image Classification with State Space Model [25.32283897448209]
  We introduce RSMamba, a novel architecture for remote sensing image classification. RSMamba is based on the State Space Model (SSM) and incorporates an efficient, hardware-aware design known as the Mamba. We propose a dynamic multi-path activation mechanism to augment Mamba's capacity to model non-temporal image data.
  arXiv  Detail & Related papers  (2024-03-28T17:59:49Z)
• PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition [21.761988930589727]
  PlainMamba is a simple non-hierarchical state space model (SSM) designed for general visual recognition. We adapt the selective scanning process of Mamba to the visual domain, enhancing its ability to learn features from two-dimensional images. Our architecture is designed to be easy to use and easy to scale, formed by stacking identical PlainMamba blocks.
  arXiv  Detail & Related papers  (2024-03-26T13:35:10Z)
• Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining [85.08169822181685]
  This paper introduces a novel Mamba-based model, Swin-UMamba, designed specifically for medical image segmentation tasks. Swin-UMamba demonstrates superior performance with a large margin compared to CNNs, ViTs, and latest Mamba-based models.
  arXiv  Detail & Related papers  (2024-02-05T18:58:11Z)
• VMamba: Visual State Space Model [98.0517369083152]
  We adapt Mamba, a state-space language model, into VMamba, a vision backbone with linear time complexity.<n>At the core of VMamba is a stack of Visual State-Space (VSS) blocks with the 2D Selective Scan (SS2D) module.
  arXiv  Detail & Related papers  (2024-01-18T17:55:39Z)
• Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model [48.233300343211205]
  We propose a new generic vision backbone with bidirectional Mamba blocks (Vim) Vim marks the image sequences with position embeddings and compresses the visual representation with bidirectional state space models. The results demonstrate that Vim is capable of overcoming the computation & memory constraints on performing Transformer-style understanding for high-resolution images.
  arXiv  Detail & Related papers  (2024-01-17T18:56:18Z)

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.