Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

• URL: http://arxiv.org/abs/2401.09417v2
• Date: Sat, 10 Feb 2024 14:28:20 GMT
• Title: Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
• Authors: Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, Xinggang Wang
• Abstract summary: 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.
• Score: 51.10876815815515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., the Mamba deep learning model, have shown great potential for long sequence modeling. Meanwhile building efficient and generic vision backbones purely upon SSMs is an appealing direction. However, representing visual data is challenging for SSMs due to the position-sensitivity of visual data and the requirement of global context for visual understanding. In this paper, we show that the reliance on self-attention for visual representation learning is not necessary and propose a new generic vision backbone with bidirectional Mamba blocks (Vim), which marks the image sequences with position embeddings and compresses the visual representation with bidirectional state space models. On ImageNet classification, COCO object detection, and ADE20k semantic segmentation tasks, Vim achieves higher performance compared to well-established vision transformers like DeiT, while also demonstrating significantly improved computation & memory efficiency. For example, Vim is 2.8$\times$ faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on images with a resolution of 1248$\times$1248. The results demonstrate that Vim is capable of overcoming the computation & memory constraints on performing Transformer-style understanding for high-resolution images and it has great potential to be the next-generation backbone for vision foundation models. Code is available at https://github.com/hustvl/Vim.

Related papers

• 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)
• MSVM-UNet: Multi-Scale Vision Mamba UNet for Medical Image Segmentation [3.64388407705261]
  We propose a Multi-Scale Vision Mamba UNet model for medical image segmentation, termed MSVM-UNet. Specifically, by introducing multi-scale convolutions in the VSS blocks, we can more effectively capture and aggregate multi-scale feature representations from the hierarchical features of the VMamba encoder.
  arXiv  Detail & Related papers  (2024-08-25T06:20:28Z)
• MambaVision: A Hybrid Mamba-Transformer Vision Backbone [54.965143338206644]
  We propose a novel hybrid Mamba-Transformer backbone, denoted as MambaVision, which is specifically tailored for vision applications. Our core contribution includes redesigning the Mamba formulation to enhance its capability for efficient modeling of visual features. We conduct a comprehensive ablation study on the feasibility of integrating Vision Transformers (ViT) with Mamba.
  arXiv  Detail & Related papers  (2024-07-10T23:02:45Z)
• Vim-F: Visual State Space Model Benefiting from Learning in the Frequency Domain [9.458951424465605]
  State Space Models (SSMs) with efficient hardware-aware designs, known as the Mamba deep learning models, have made significant progress in modeling long sequences. We propose a novel model called Vim-F, which employs pure Mamba encoders and scans in both the frequency and spatial domains.
  arXiv  Detail & Related papers  (2024-05-29T01:01:19Z)
• Efficient Visual State Space Model for Image Deblurring [83.57239834238035]
  Convolutional neural networks (CNNs) and Vision Transformers (ViTs) have achieved excellent performance in image restoration. We propose a simple yet effective visual state space model (EVSSM) for image deblurring.
  arXiv  Detail & Related papers  (2024-05-23T09:13:36Z)
• Vim4Path: Self-Supervised Vision Mamba for Histopathology Images [9.271739983963458]
  This paper proposes leveraging the Vision Mamba (Vim) architecture, inspired by state space models, within the DINO framework for representation learning in computational pathology. We evaluate the performance of Vim against Vision Transformers (ViT) on the Camelyon16 dataset for both patch-level and slide-level classification.
  arXiv  Detail & Related papers  (2024-04-20T00:44:40Z)
• LocalMamba: Visual State Space Model with Windowed Selective Scan [45.00004931200446]
  Key to enhancing Vision Mamba (ViM) lies in optimizing scan directions for sequence modeling. We introduce a novel local scanning strategy that divides images into distinct windows, effectively capturing local dependencies. Our model significantly outperforms Vim-Ti by 3.1% on ImageNet with the same 1.5G FLOPs.
  arXiv  Detail & Related papers  (2024-03-14T12:32:40Z)
• VMamba: Visual State Space Model [92.83984290020891]
  VMamba is a vision backbone that works in linear time complexity. At the core of VMamba lies 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 Permutator: A Permutable MLP-Like Architecture for Visual Recognition [185.80889967154963]
  We present Vision Permutator, a conceptually simple and data efficient-like architecture for visual recognition. By realizing the importance of the positional information carried by 2D feature representations, Vision Permutator encodes the feature representations along the height and width dimensions with linear projections. We show that our Vision Permutators are formidable competitors to convolutional neural networks (CNNs) and vision transformers.
  arXiv  Detail & Related papers  (2021-06-23T13:05:23Z)

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.