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Mamba

Papers

Showing 251275 of 1058 papers

TitleStatusHype
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter EfficientCode1
Does Transformer Interpretability Transfer to RNNs?Code1
A Lightweight and Effective Image Tampering Localization Network with Vision MambaCode1
MambaSOD: Dual Mamba-Driven Cross-Modal Fusion Network for RGB-D Salient Object DetectionCode1
DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote SensingCode1
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI ReconstructionCode1
MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for MambaCode1
Mamba or Transformer for Time Series Forecasting? Mixture of Universals (MoU) Is All You NeedCode1
Mamba-Spike: Enhancing the Mamba Architecture with a Spiking Front-End for Efficient Temporal Data ProcessingCode1
MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T TrackingCode1
SIGMA: Selective Gated Mamba for Sequential RecommendationCode1
MAMBA: Multi-level Aggregation via Memory Bank for Video Object DetectionCode1
DiMSUM: Diffusion Mamba -- A Scalable and Unified Spatial-Frequency Method for Image GenerationCode1
MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image SegmentationCode1
MambaLRP: Explaining Selective State Space Sequence ModelsCode1
Mamba meets crack segmentationCode1
MambaNUT: Nighttime UAV Tracking via Mamba-based Adaptive Curriculum LearningCode1
Differential MambaCode1
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics TasksCode1
AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual SegmentationCode1
DGMamba: Domain Generalization via Generalized State Space ModelCode1
Detail Matters: Mamba-Inspired Joint Unfolding Network for Snapshot Spectral Compressive ImagingCode1
MambaST: A Plug-and-Play Cross-Spectral Spatial-Temporal Fuser for Efficient Pedestrian DetectionCode1
MambaLCT: Boosting Tracking via Long-term Context State Space ModelCode1
nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image SegmentationCode1
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