HiRo: A Compact Four-Directional Hierarchical Reservoir Token-Mixer for Efficient Image Classification
arXiv:2606.15151v1 Announce Type: cross Abstract: Recent image classification models must balance local feature modeling, cross-window interaction, and parameter efficiency. Many high-performing architectures rely on fully trainable token-mixers, which improve representation learning but increase parameter count, optimization complexity and computational cost. We propose a parameter-efficient image classification model called HiRo that integrates shifted-window partitioning with multi-directional hierarchical reservoir computing. Images are divided into non-overlapping patches (treated as toke
The continuous push for more efficient and less computationally expensive AI models drives innovation in image classification architectures. This research addresses the persistent challenge of balancing performance with practical resource usage.
Development of more parameter-efficient models reduces the computational and energy costs associated with advanced AI, broadening accessibility and deployment potential across various industries including edge computing and resource-constrained environments.
This research suggests a potential shift towards more compact and efficient deep learning models, enabling broader application of image classification where computational power or energy consumption is a limiting factor.
- · AI researchers
- · Hardware manufacturers (for AI accelerators)
- · Companies deploying AI on edge devices
- · Cloud providers (potentially lower cost per inference)
- · Developers reliant solely on large, computationally intensive models
More efficient image classification leads to lower operational costs for AI-driven applications.
Increased adoption of AI in sectors previously constrained by compute or energy due to the availability of efficient models.
Democratization of advanced AI capabilities, potentially leading to new applications and shifts in competitive landscapes as smaller players can afford sophisticated AI.
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Read at arXiv cs.LG