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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Plainmamba: Improving non- hierarchical mamba in visual recognition
14 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 14representative citing papers
MbaGCN combines message aggregation, selective state space transitions, and node state prediction to create a more scalable deep graph convolutional network.
LFNet fuses complementary representations from state space and convolutional backbones using liquid fusion and saliency-guided upsampling to reach state-of-the-art performance on five salient object detection tasks.
STORM is a training-free spatial-aware token reduction framework that reformulates compression on spatial units to preserve grid topology and neighborhood coherence in visual state space models.
C-GSPN scales 2D spatial propagation to foundation vision encoders via a fast CUDA kernel, compressed blocks, and two-stage distillation, matching ViT performance with 15% fewer parameters and 4x block speedup at 2K resolution.
Deformba introduces context-adaptive state fusion to vision SSMs for better spatial augmentation and cross-stream interactions, showing strong results on 2D classification/detection/segmentation and 3D BEV perception benchmarks.
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.
SCRWKV is a 1.22M-parameter Vision-RWKV model using Structure-Field Encoder with AMCM and SCIU modules plus CSHF decoder that reports F1 0.8428 and mIoU 0.8512 on TUT crack dataset while claiming to outperform prior SOTA.
TopoMamba improves medical image segmentation by combining topology-aware diagonal scans with standard cross-scans and a HSIC Gate for efficient fusion, yielding gains on thin and curved targets like the pancreas.
MambaADv2 evolves Mamba state space models with hybrid blocks, frequency convolutions, and adaptive scanning for improved unsupervised anomaly detection.
Benchmarks Vision Mamba variants for AI-generated image detection against CNN, ViT, and VLM detectors on diverse datasets and synthetic sources, reporting promise alongside limitations.
Bilinear discretization improves Vision Mamba accuracy over zero-order hold on classification, segmentation, and detection benchmarks with only modest extra training cost.
MDDCNet combines Mamba blocks with deformable dilated convolutions, enhanced feed-forward networks, and an attention-aggregating feature pyramid to achieve better multi-scale traffic object detection than prior detectors.
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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Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
MbaGCN combines message aggregation, selective state space transitions, and node state prediction to create a more scalable deep graph convolutional network.
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Liquid Fusion of Heterogeneous Representations Towards General Salient Object Detection
LFNet fuses complementary representations from state space and convolutional backbones using liquid fusion and saliency-guided upsampling to reach state-of-the-art performance on five salient object detection tasks.
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Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models
STORM is a training-free spatial-aware token reduction framework that reformulates compression on spatial units to preserve grid topology and neighborhood coherence in visual state space models.
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Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders
C-GSPN scales 2D spatial propagation to foundation vision encoders via a fast CUDA kernel, compressed blocks, and two-stage distillation, matching ViT performance with 15% fewer parameters and 4x block speedup at 2K resolution.
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Deformba: Vision State Space Model with Adaptive State Fusion
Deformba introduces context-adaptive state fusion to vision SSMs for better spatial augmentation and cross-stream interactions, showing strong results on 2D classification/detection/segmentation and 3D BEV perception benchmarks.
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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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SCRWKV: Ultra-Compact Structure-Calibrated Vision-RWKV for Topological Crack Segmentation
SCRWKV is a 1.22M-parameter Vision-RWKV model using Structure-Field Encoder with AMCM and SCIU modules plus CSHF decoder that reports F1 0.8428 and mIoU 0.8512 on TUT crack dataset while claiming to outperform prior SOTA.
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TopoMamba: Topology-Aware Scanning and Fusion for Segmenting Heterogeneous Medical Visual Media
TopoMamba improves medical image segmentation by combining topology-aware diagonal scans with standard cross-scans and a HSIC Gate for efficient fusion, yielding gains on thin and curved targets like the pancreas.
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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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Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation
Benchmarks Vision Mamba variants for AI-generated image detection against CNN, ViT, and VLM detectors on diverse datasets and synthetic sources, reporting promise alongside limitations.
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Beyond ZOH: Advanced Discretization Strategies for Vision Mamba
Bilinear discretization improves Vision Mamba accuracy over zero-order hold on classification, segmentation, and detection benchmarks with only modest extra training cost.
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Beyond Mamba: Enhancing State-space Models with Deformable Dilated Convolutions for Multi-scale Traffic Object Detection
MDDCNet combines Mamba blocks with deformable dilated convolutions, enhanced feed-forward networks, and an attention-aggregating feature pyramid to achieve better multi-scale traffic object detection than prior detectors.
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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.