Beyond ZOH: Advanced Discretization Strategies for Vision Mamba

2026-04-22 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

Vision Mamba, an SSM-based vision model, typically uses zero-order hold (ZOH) discretization, which limits its accuracy in dynamic visual tasks by assuming constant input signals between samples. This research systematically compares six discretization schemes within the Vision Mamba framework: ZOH, first-order hold (FOH), bilinear/Tustin transform (BIL), polynomial interpolation (POL), higher-order hold (HOH), and the fourth-order Runge-Kutta method (RK4). The methods were evaluated on standard visual benchmarks for image classification, semantic segmentation, and object detection. Results indicate that POL and HOH significantly improve accuracy but require more training computation. The bilinear transform (BIL) offers consistent accuracy gains over ZOH with only modest computational overhead, presenting the best balance of precision and efficiency among the tested methods.

Key takeaway

For research scientists developing or deploying SSM-based vision architectures, you should consider replacing the default zero-order hold (ZOH) discretization with the bilinear transform (BIL). This change offers consistent accuracy improvements across tasks like image classification and object detection with only a modest increase in computational overhead, making it a strong candidate for a new baseline in state-of-the-art models.

Key insights

Advanced discretization methods significantly enhance Vision Mamba's accuracy in dynamic visual tasks.

Principles

• ZOH limits temporal fidelity in SSMs.
• Discretization choice impacts SSM accuracy.
• Higher-order methods improve precision.

Method

Six discretization schemes (ZOH, FOH, BIL, POL, HOH, RK4) were systematically compared within Vision Mamba on image classification, semantic segmentation, and object detection benchmarks.

In practice

• Adopt BIL for improved SSM performance.
• Consider POL/HOH for maximum accuracy.
• Evaluate trade-offs between precision and efficiency.

Topics

• Vision Mamba
• State Space Models
• Discretization Strategies
• Bilinear Transform
• Polynomial Interpolation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.