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 degradation to the spatially agnostic nature of existing reduction methods, which violate the two-dimensional structural premise required by the selective scanning mechanism. In this work, we propose STORM, a spatial-aware token reduction framework designed to maintain structural integrity throughout the compression process.
The paper addresses a critical limitation in Mamba variants, a relatively new and promising architecture for efficient visual sequence modeling, as researchers actively explore its capabilities and deficiencies.
Improving the efficiency and faithfulness of visual state space models like Mamba is crucial for deploying advanced AI in real-world applications where computational resources are constrained and performance is paramount.
The proposed STORM framework offers a method to maintain structural integrity during token reduction in Mamba variants, potentially unlocking their full potential for efficient visual processing without performance degradation.
- · AI compute and infrastructure providers
- · Developers of VSSM-based AI applications
- · Computer vision researchers
- · Inefficient visual AI models
- · Users with limited computational resources tied to older inefficient models
More efficient and accurate visual AI models become available, accelerating research and development.
Reduced computational costs for visual AI applications, making advanced computer vision more accessible and widespread.
Proliferation of AI agents and autonomous systems that rely on real-time, efficient visual understanding.
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Read at arXiv cs.AI