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New PRISMamba method enhances Vision SSMs with rotation robustness

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PRISMamba method enhances Vision SSMs with rotation robustness

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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]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yi-Kuan Hsieh, Kuan-Chuan Peng, Xin li, Ming-Ching Chang, Yu-Chee Tseng, Jun-Wei Hsieh ·

    Partial Ring Scan: Revisiting Scan Order in Vision State Space Models

    arXiv:2602.04170v2 Announce Type: replace Abstract: State Space Models (SSMs) have emerged as efficient alternatives to attention for vision tasks, offering lineartime sequence processing with competitive accuracy. Vision SSMs, however, require serializing 2D images into 1D token…

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