[Mamba-Survey-2024] Paper list for State-Space-Model/Mamba and it's Applications
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Updated
Jul 18, 2026
[Mamba-Survey-2024] Paper list for State-Space-Model/Mamba and it's Applications
Mamba in Vision: A Comprehensive Survey of Techniques and Applications
[CVPR 25] Official Implementation (Pytorch) of "EfficientViM: Efficient Vision Mamba with Hidden State Mixer-based State Space Duality"
[ACM MM'24 Oral] RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining
[WACV2025 Oral] SUM: Saliency Unification through Mamba for Visual Attention Modeling
[ICLR 2024 Oral] Less is More: Fewer Interpretable Region via Submodular Subset Selection
[NeurIPS 2024] Official implementation of the paper "MambaLRP: Explaining Selective State Space Sequence Models" 🐍
List of papers related to State Space Models (Mamba) in Vision.
[NeurIPS 2024] MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging
Event Stream based Sign-Language-Translation
Official implementation of the paper "MFil-Mamba: Multi-Filter Scanning for Spatial Redundancy-Aware Visual State Space Models"
Scalable Algorithm-Hardware Co-Design for Dynamically Quantized Vision Mamba Models Inference on FPGA.
StageMamba is a deep learning framework for multi-class eye disease classification from retinal fundus images. It combines EfficientNet-B4, Multi-Level Feature Fusion (MLFF), and Mamba-based Vision State Space blocks to capture both local retinal details and global disease patterns, achieving robust and efficient diagnosis across 10 eye disease
A comparative Generative AI framework for conditional face synthesis. Features a custom autoregressive Vision Mamba (SSM) architecture, benchmarked alongside Conditional DDPM and VAE models on the CelebA dataset.
PyTorch code for Efficient Vision Mamba MRI super-resolution, Medical Physics 2026.
Comparative study of CNN, Transformer, and SSM-based models for multi-domain image classification tasks. Developed as the undergraduate thesis project of Group-T2410196 at BRAC University, Fall 2024.
[IKT 2024] A Multi-Task Framework Using Mamba for Identity, Age, and Gender Classification from Hand Images
Implementation of a Vision-Mamba network, integrating State Space Models (SSM) with a patch-based encoder–decoder for image inpainting, colorization, and denoising. Trained with L1, SSIM, and VGG perceptual losses to preserve both structure and perceptual realism.
The associated code with the paper "Vision Mamba for Accurate and Efficient Alzheimer’s Disease Classification via Brain MRI".
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