New PRISMamba method enhances Vision SSMs with rotation robustness

Researchers have introduced PRISMamba, a novel approach to processing images within Vision State Space Models (SSMs). Unlike traditional methods that serialize images into linear sequences, PRISMamba partitions images into concentric rings and aggregates information within each ring. This method enhances rotation robustness and improves efficiency by selectively filtering channels. PRISMamba achieves competitive accuracy on ImageNet-1K while demonstrating superior throughput and fewer FLOPs compared to existing VMamba models, particularly maintaining performance under rotational transformations. AI

IMPACT Introduces a more rotationally robust and efficient method for processing images in Vision SSMs, potentially improving performance in applications sensitive to spatial orientation.

RANK_REASON The cluster contains an academic paper detailing a new method for Vision State Space Models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• A100
• ImageNet-1K
• PRISMamba
• State Space Models
• Vision State Space Models
• VMamba
• YI Kuan Hsieh

• paper
• model release

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