ViM-Q delivers 4.96x speedup and 59.8x energy efficiency for Vision Mamba inference on FPGA versus a quantized GPU baseline using dynamic activation quantization, per-block APoT weights, and a pipelined SSM engine.
Mamba in vision: A comprehensive survey of techniques and applications
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
ABMamba uses Mamba-based linear-complexity processing plus a novel Aligned Hierarchical Bidirectional Scan to deliver competitive video captioning on VATEX and MSR-VTT at roughly 3x higher throughput than typical Transformer MLLMs.
MambaLiteUNet integrates Mamba into U-Net with adaptive fusion, local-global mixing, and cross-gated attention modules to reach 87.12% IoU and 93.09% Dice on skin lesion datasets while cutting parameters by 93.6%.
HiFi-Mamba uses stacked W-Laplacian spectral decoupling and unidirectional HiFi-Mamba blocks to improve high-frequency detail preservation and efficiency over prior Mamba, CNN, and Transformer models for MRI reconstruction.
citing papers explorer
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ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA
ViM-Q delivers 4.96x speedup and 59.8x energy efficiency for Vision Mamba inference on FPGA versus a quantized GPU baseline using dynamic activation quantization, per-block APoT weights, and a pipelined SSM engine.
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ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
ABMamba uses Mamba-based linear-complexity processing plus a novel Aligned Hierarchical Bidirectional Scan to deliver competitive video captioning on VATEX and MSR-VTT at roughly 3x higher throughput than typical Transformer MLLMs.
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MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation
MambaLiteUNet integrates Mamba into U-Net with adaptive fusion, local-global mixing, and cross-gated attention modules to reach 87.12% IoU and 93.09% Dice on skin lesion datasets while cutting parameters by 93.6%.
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HiFi-Mamba: Dual-Stream W-Laplacian Enhanced Mamba for High-Fidelity MRI Reconstruction
HiFi-Mamba uses stacked W-Laplacian spectral decoupling and unidirectional HiFi-Mamba blocks to improve high-frequency detail preservation and efficiency over prior Mamba, CNN, and Transformer models for MRI reconstruction.