A Separable Self-attention Inspired by the State Space Model for Computer Vision

• URL: http://arxiv.org/abs/2501.02040v1
• Date: Fri, 03 Jan 2025 15:23:36 GMT
• Title: A Separable Self-attention Inspired by the State Space Model for Computer Vision
• Authors: Juntao Zhang, Shaogeng Liu, Kun Bian, You Zhou, Pei Zhang, Jianning Liu, Jun Zhou, Bingyan Liu,
• Abstract summary: Mamba is an efficient State Space Model with linear computational complexity. Recent studies have shown that there is a rich theoretical connection between state space models and attention variants. We propose a novel separable self attention method, for the first time introducing some excellent design concepts of Mamba into separable self-attention.
• Score: 9.958579689420253
• License:
• Abstract: Mamba is an efficient State Space Model (SSM) with linear computational complexity. Although SSMs are not suitable for handling non-causal data, Vision Mamba (ViM) methods still demonstrate good performance in tasks such as image classification and object detection. Recent studies have shown that there is a rich theoretical connection between state space models and attention variants. We propose a novel separable self attention method, for the first time introducing some excellent design concepts of Mamba into separable self-attention. To ensure a fair comparison with ViMs, we introduce VMINet, a simple yet powerful prototype architecture, constructed solely by stacking our novel attention modules with the most basic down-sampling layers. Notably, VMINet differs significantly from the conventional Transformer architecture. Our experiments demonstrate that VMINet has achieved competitive results on image classification and high-resolution dense prediction tasks.Code is available at: \url{https://github.com/yws-wxs/VMINet}.

Related papers

• Exploring Token Pruning in Vision State Space Models [38.122017567843905]
  State Space Models (SSMs) have the advantage of keeping linear computational complexity compared to attention modules in transformers. We take the novel step of enhancing the efficiency of SSM-based vision models through token-based pruning. We achieve 81.7% accuracy on ImageNet with a 41.6% reduction in the FLOPs for pruned PlainMamba-L3.
  arXiv  Detail & Related papers  (2024-09-27T17:59:50Z)
• 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)
• Demystify Mamba in Vision: A Linear Attention Perspective [72.93213667713493]
  Mamba is an effective state space model with linear computation complexity. We show that Mamba shares surprising similarities with linear attention Transformer. We propose a Mamba-Inspired Linear Attention (MILA) model by incorporating the merits of these two key designs into linear attention.
  arXiv  Detail & Related papers  (2024-05-26T15:31:09Z)
• The Hidden Attention of Mamba Models [54.50526986788175]
  The Mamba layer offers an efficient selective state space model (SSM) that is highly effective in modeling multiple domains. We show that such models can be viewed as attention-driven models. This new perspective enables us to empirically and theoretically compare the underlying mechanisms to that of the self-attention layers in transformers.
  arXiv  Detail & Related papers  (2024-03-03T18:58:21Z)
• PointMamba: A Simple State Space Model for Point Cloud Analysis [65.59944745840866]
  We propose PointMamba, transferring the success of Mamba, a recent representative state space model (SSM), from NLP to point cloud analysis tasks. Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, presenting global modeling capacity while significantly reducing computational costs.
  arXiv  Detail & Related papers  (2024-02-16T14:56:13Z)
• VMamba: Visual State Space Model [98.0517369083152]
  We adapt Mamba, a state-space language model, into VMamba, a vision backbone with linear time complexity. 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.