用四元数近似实现旋转不变的图像分类与目标检测，更高效
Quaternion Approximation Networks for Enhanced Image Classification and Oriented Object Detection
- 通过实数运算近似四元数卷积，保持几何特性同时提升效率
- 在CIFAR、ImageNet上参数更少、收敛更快，准确率更高
- 适合资源受限机器人系统，尤其需要处理旋转目标的场景
本文提出四元数近似网络（QUAN），一种基于四元数代数的深度学习框架，用于实现旋转等变的图像分类与目标检测。不同于完全在四元数域操作的传统方法，QUAN通过实数运算分解哈密顿乘积来近似四元数卷积，既保留几何特性又支持高效实现（使用自定义CUDA核）。引入独立四元数批归一化（IQBN）提升训练稳定性，并将四元数操作扩展至空间注意力机制。QUAN在图像分类（CIFAR-10/100、ImageNet）、目标检测（COCO、DOTA）及机器人感知任务中评估。分类任务中，相比现有卷积与四元数模型，参数更少、收敛更快、准确率更高；目标检测方面，相比标准CNN，在参数效率和旋转处理上表现更优，并在该下游任务中达到四元数CNN的SOTA水平。结果表明其在资源受限的旋转感知系统中具有部署潜力。
原文摘要 · Abstract (English)
This paper introduces Quaternion Approximate Networks (QUAN), a novel deep learning framework that leverages quaternion algebra for rotation equivariant image classification and object detection. Unlike conventional quaternion neural networks attempting to operate entirely in the quaternion domain, QUAN approximates quaternion convolution through Hamilton product decomposition using real-valued operations. This approach preserves geometric properties while enabling efficient implementation with custom CUDA kernels. We introduce Independent Quaternion Batch Normalization (IQBN) for training stability and extend quaternion operations to spatial attention mechanisms. QUAN is evaluated on image classification (CIFAR-10/100, ImageNet), object detection (COCO, DOTA), and robotic perception tasks. In classification tasks, QUAN achieves higher accuracy with fewer parameters and faster convergence compared to existing convolution and quaternion-based models. For objection detection, QUAN demonstrates improved parameter efficiency and rotation handling over standard Convolutional Neural Networks (CNNs) while establishing the SOTA for quaternion CNNs in this downstream task. These results highlight its potential for deployment in resource-constrained robotic systems requiring rotation-aware perception and application in other domains.
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