FractalMamba++ scales Vision Mamba across resolutions by using Hilbert fractal serialization, hierarchy-based skip connections, and fractal-aware 2D rotary position encoding.
Mamba-r: Vision mamba also needs registers
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UNVERDICTED 3representative citing papers
HAMSA achieves 85.7% ImageNet-1K top-1 accuracy as a spectral-domain SSM with 2.2x faster inference and lower memory than transformers or scanning-based SSMs.
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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FractalMamba++: Scaling Vision Mamba Across Resolutions via Hilbert Fractal Geometry
FractalMamba++ scales Vision Mamba across resolutions by using Hilbert fractal serialization, hierarchy-based skip connections, and fractal-aware 2D rotary position encoding.
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HAMSA: Scanning-Free Vision State Space Models via SpectralPulseNet
HAMSA achieves 85.7% ImageNet-1K top-1 accuracy as a spectral-domain SSM with 2.2x faster inference and lower memory than transformers or scanning-based SSMs.
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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.