MHS-VM: Multi-Head Scanning in Parallel Subspaces for Vision Mamba

• URL: http://arxiv.org/abs/2406.05992v1
• Date: Mon, 10 Jun 2024 03:24:43 GMT
• Title: MHS-VM: Multi-Head Scanning in Parallel Subspaces for Vision Mamba
• Authors: Zhongping Ji,
• Abstract summary: State Space Models (SSMs) with Mamba have shown great promise for long-range dependency modeling with linear complexity. To effectively organize and construct visual features within the 2D image space through 1D selective scan, we propose a novel Multi-Head Scan (MHS) module. The resulting sub-embeddings, obtained from the multi-head scan process, are then integrated and ultimately projected back into the high-dimensional space.
• Score: 0.43512163406552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, State Space Models (SSMs), with Mamba as a prime example, have shown great promise for long-range dependency modeling with linear complexity. Then, Vision Mamba and the subsequent architectures are presented successively, and they perform well on visual tasks. The crucial step of applying Mamba to visual tasks is to construct 2D visual features in sequential manners. To effectively organize and construct visual features within the 2D image space through 1D selective scan, we propose a novel Multi-Head Scan (MHS) module. The embeddings extracted from the preceding layer are projected into multiple lower-dimensional subspaces. Subsequently, within each subspace, the selective scan is performed along distinct scan routes. The resulting sub-embeddings, obtained from the multi-head scan process, are then integrated and ultimately projected back into the high-dimensional space. Moreover, we incorporate a Scan Route Attention (SRA) mechanism to enhance the module's capability to discern complex structures. To validate the efficacy of our module, we exclusively substitute the 2D-Selective-Scan (SS2D) block in VM-UNet with our proposed module, and we train our models from scratch without using any pre-trained weights. The results indicate a significant improvement in performance while reducing the parameters of the original VM-UNet. The code for this study is publicly available at https://github.com/PixDeep/MHS-VM.

Related papers

• Spatial-Mamba: Effective Visual State Space Models via Structure-Aware State Fusion [46.82975707531064]
  Selective state space models (SSMs) excel at capturing long-range dependencies in 1D sequential data. We propose Spatial-Mamba, a novel approach that establishes neighborhood connectivity directly in the state space. We show that Spatial-Mamba, even with a single scan, attains or surpasses the state-of-the-art SSM-based models in image classification, detection and segmentation.
  arXiv  Detail & Related papers  (2024-10-19T12:56:58Z)
• 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)
• GroupMamba: Parameter-Efficient and Accurate Group Visual State Space Model [66.35608254724566]
  State-space models (SSMs) have showcased effective performance in modeling long-range dependencies with subquadratic complexity. However, pure SSM-based models still face challenges related to stability and achieving optimal performance on computer vision tasks. Our paper addresses the challenges of scaling SSM-based models for computer vision, particularly the instability and inefficiency of large model sizes.
  arXiv  Detail & Related papers  (2024-07-18T17:59:58Z)
• 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)
• 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 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)
• Multi-level Second-order Few-shot Learning [111.0648869396828]
  We propose a Multi-level Second-order (MlSo) few-shot learning network for supervised or unsupervised few-shot image classification and few-shot action recognition. We leverage so-called power-normalized second-order base learner streams combined with features that express multiple levels of visual abstraction. We demonstrate respectable results on standard datasets such as Omniglot, mini-ImageNet, tiered-ImageNet, Open MIC, fine-grained datasets such as CUB Birds, Stanford Dogs and Cars, and action recognition datasets such as HMDB51, UCF101, and mini-MIT.
  arXiv  Detail & Related papers  (2022-01-15T19:49:00Z)

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.