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.
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