EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba

• URL: http://arxiv.org/abs/2403.09977v1
• Date: Fri, 15 Mar 2024 02:48:47 GMT
• Title: EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba
• Authors: Xiaohuan Pei, Tao Huang, Chang Xu,
• Abstract summary: This work proposes to explore the potential of visual state space models in light-weight model design and introduce a novel efficient model variant dubbed EfficientVMamba. Our EfficientVMamba integrates a atrous-based selective scan approach by efficient skip sampling, constituting building blocks designed to harness both global and local representational features. Experimental results show that, EfficientVMamba scales down the computational complexity while yields competitive results across a variety of vision tasks.
• Score: 19.062950348441426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prior efforts in light-weight model development mainly centered on CNN and Transformer-based designs yet faced persistent challenges. CNNs adept at local feature extraction compromise resolution while Transformers offer global reach but escalate computational demands $\mathcal{O}(N^2)$. This ongoing trade-off between accuracy and efficiency remains a significant hurdle. Recently, state space models (SSMs), such as Mamba, have shown outstanding performance and competitiveness in various tasks such as language modeling and computer vision, while reducing the time complexity of global information extraction to $\mathcal{O}(N)$. Inspired by this, this work proposes to explore the potential of visual state space models in light-weight model design and introduce a novel efficient model variant dubbed EfficientVMamba. Concretely, our EfficientVMamba integrates a atrous-based selective scan approach by efficient skip sampling, constituting building blocks designed to harness both global and local representational features. Additionally, we investigate the integration between SSM blocks and convolutions, and introduce an efficient visual state space block combined with an additional convolution branch, which further elevate the model performance. Experimental results show that, EfficientVMamba scales down the computational complexity while yields competitive results across a variety of vision tasks. For example, our EfficientVMamba-S with $1.3$G FLOPs improves Vim-Ti with $1.5$G FLOPs by a large margin of $5.6\%$ accuracy on ImageNet. Code is available at: \url{https://github.com/TerryPei/EfficientVMamba}.

Related papers

• HRVMamba: High-Resolution Visual State Space Model for Dense Prediction [60.80423207808076]
  State Space Models (SSMs) with efficient hardware-aware designs have demonstrated significant potential in computer vision tasks. These models have been constrained by three key challenges: insufficient inductive bias, long-range forgetting, and low-resolution output representation. We introduce the Dynamic Visual State Space (DVSS) block, which employs deformable convolution to mitigate the long-range forgetting problem. We also introduce High-Resolution Visual State Space Model (HRVMamba) based on the DVSS block, which preserves high-resolution representations throughout the entire process.
  arXiv  Detail & Related papers  (2024-10-04T06:19:29Z)
• LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba [54.85262314960038]
  Local Attentional Mamba blocks capture both global contexts and local details with linear complexity. Our model exhibits exceptional scalability and surpasses the performance of DiT across various model scales on ImageNet at 256x256 resolution. Compared to state-of-the-art diffusion models on ImageNet 256x256 and 512x512, our largest model presents notable advantages, such as a reduction of up to 62% GFLOPs.
  arXiv  Detail & Related papers  (2024-08-05T16:39:39Z)
• 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)
• Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model [18.30032389736101]
  Mamba model, based on state space models (SSM), outperforms Transformer in multiple areas with only linear complexity. We present Mamba3D, a state space model tailored for point cloud learning to enhance local feature extraction.
  arXiv  Detail & Related papers  (2024-04-23T12:20:27Z)
• MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection [72.46396769642787]
  We develop a nested structure, Mamba-in-Mamba (MiM-ISTD), for efficient infrared small target detection. MiM-ISTD is $8 times$ faster than the SOTA method and reduces GPU memory usage by 62.2$%$ when testing on $2048 times 2048$ images.
  arXiv  Detail & Related papers  (2024-03-04T15:57:29Z)
• 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 [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)
• FastSal: a Computationally Efficient Network for Visual Saliency Prediction [7.742198347952173]
  We show that MobileNetV2 makes an excellent backbone for a visual saliency model and can be effective even without a complex decoder. We also show that knowledge transfer from a more computationally expensive model like DeepGaze II can be achieved via pseudo-labelling an unlabelled dataset.
  arXiv  Detail & Related papers  (2020-08-25T16:32:33Z)

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.