✨✨ Latest Papers on Mamba

• Mamba: Linear-Time Sequence Modeling with Selective State Spaces [arxiv] [code]

• Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model [arxiv] [code1] [code2]
• U-shaped Vision Mamba for Single Image Dehazing [arxiv] [code]
• Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data [arxiv]

• VMamba: Visual State Space Model [arxiv] [code]
• Scalable Diffusion Models with State Space Backbone [arxiv] [code]

• U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation [arxiv] [code]
• SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation [arxiv] [code]
• MambaMorph: a Mamba-based Backbone with Contrastive Feature Learning for Deformable MR-CT Registration [arxiv] [code]
• Vivim: a Video Vision Mamba for Medical Video Object Segmentation [arxiv] [code]
• VM-UNet: Vision Mamba UNet for Medical Image Segmentation [arxiv] [code]
• Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining [arxiv] [code]
• Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation [arxiv] [code]
• FD-Vision Mamba for Endoscopic Exposure Correction [arxiv] [code]
• Semi-Mamba-UNet: Pixel-Level Contrastive Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation [arxiv] [code]
• P-Mamba: Marrying Perona Malik Diffusion with Mamba for Efficient Pediatric Echocardiographic Left Ventricular Segmentation [arxiv]

• nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model [arxiv] [code]

• Gated Linear Attention Transformers with Hardware-Efficient Training [arxiv] [code]
• MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts [arxiv] [code]
• MambaTab: A Simple Yet Effective Approach for Handling Tabular Data [arxiv] [code]
• MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection [arxiv] [code]
• MambaByte: Token-free Selective State Space Model [arxiv] [code]
• Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces [arxiv] [code]
• BlackMamba: Mixture of Experts for State-Space Models [arxiv] [code]
• Is Mamba Capable of In-Context Learning? [arxiv] [code]
• Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks [arxiv] [code]
• Graph Mamba: Towards Learning on Graphs with State Space Models [arxiv] [code]
• Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling [arxiv] [code]