RS4D distills ViT knowledge into SSM backbones for remote sensing instance segmentation, delivering 8x fewer parameters and 9x fewer FLOPs than ViT methods while matching or exceeding accuracy on SSDD, WHU, and NWPU datasets.
Videomamba: State space model for efficient video understanding,
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MambaADv2 evolves Mamba state space models with hybrid blocks, frequency convolutions, and adaptive scanning for improved unsupervised anomaly detection.
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
citing papers explorer
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Efficient Remote Sensing Instance Segmentation with Linear-Time State Space Distilled Visual Foundation Models
RS4D distills ViT knowledge into SSM backbones for remote sensing instance segmentation, delivering 8x fewer parameters and 9x fewer FLOPs than ViT methods while matching or exceeding accuracy on SSDD, WHU, and NWPU datasets.
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MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection
MambaADv2 evolves Mamba state space models with hybrid blocks, frequency convolutions, and adaptive scanning for improved unsupervised anomaly detection.
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A Survey of Mamba
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.