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New STORM framework enhances Mamba models by preserving spatial structure during token reduction

By PulseAugur Editorial · [2 sources] ·

Researchers have developed STORM, a novel spatial-aware token reduction framework designed to address performance degradation in visual state space models like Mamba when subjected to token compression. Existing reduction methods are spatially agnostic, disrupting the two-dimensional structure crucial for these models. STORM reformulates reduction as a structured operation on spatial units, preserving grid topology and neighborhood coherence without requiring additional training. This plug-and-play module significantly improves accuracy recovery, notably achieving up to a 63.3% increase on VMamba and a minimal 1.0% drop on PlainMamba, making its performance comparable to ViT. AI

IMPACT Enhances efficiency and accuracy of visual state space models, potentially improving performance in computer vision tasks.

RANK_REASON The cluster contains an academic paper detailing a new framework for improving existing models.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New STORM framework enhances Mamba models by preserving spatial structure during token reduction

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The cluster contains an academic paper detailing a new framework for improving existing models.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jindi Lv, Aoyu Li, Yuhao Zhou, Zheng Zhu, Xiaofeng Wang, Qing Ye, Yueqi Duan, Wentao Feng, Jiancheng Lv ·

    Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models

    arXiv:2606.19932v1 Announce Type: cross Abstract: Mamba demonstrates strong efficiency in modeling long visual sequences. However, when token reduction is applied to structurally enhanced Mamba variants, these models exhibit a severe performance collapse. We attribute this degrad…

  2. arXiv cs.AI TIER_1 English(EN) · Jiancheng Lv ·

    Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models

    Mamba demonstrates strong efficiency in modeling long visual sequences. However, when token reduction is applied to structurally enhanced Mamba variants, these models exhibit a severe performance collapse. We attribute this degradation to the spatially agnostic nature of existing…

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