卷积神经网络中的通道空间注意模块用于图像分类
Mohammad Zolfaghari1, Mohammad Saniee Abadeh2, Hedieh Sajedi3
1Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Scientific reports
|December 11, 2025
概括
本研究介绍了用于卷积神经网络 (CNN) 的新型并行和顺序通道空间注意模块 (PCSAM和SCSAM). 顺序通道空间注意模块 (SCSAM) 在图像分类任务中表现出卓越的效率和性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 注意力机制增强卷积神经网络 (CNN) 的图像分类.
- 以人类视觉感知为灵感的道和空间注意力模块是关键组件.
- 之前的研究还没有全面比较这些模块的并行和顺序组合.
研究的目的:
- 引入和评估两个新的通道空间注意力模块:并行通道空间注意力模块 (PCSAM) 和顺序通道空间注意力模块 (SCSAM).
- 研究道空间注意模块的最佳配置,以平衡模型效率和计算复杂性.
- 为了增强基于注意力的CNN的特征表示能力.
主要方法:
- 开发PCSAM和SCSAM,集成道注意力模块 (CAM) 和空间注意力模块 (SAM) 的子模块.
- 在CAM和SAM中利用全球平均聚合 (GAP) 和全球最大聚合 (GMP) 来进行特征提取.
- 在SAM中使用Dilation Convolution (DC),以改善Region of Interest (RoI) 的关注度.
- 在ResNet18和MobileNetv4架构中集成PCSAM和SCSAM.
- 在50个时代的CIFAR-10,CIFAR-100和Tiny-ImageNet数据集上训练和评估模型.
主要成果:
- 在所有测试的数据集中,MobileNetv4SCSAM架构实现了卓越的效率.
- 与其他评估的架构相比,MobileNetv4SCSAM显示了更高的分类性能.
- 拟议的SCSAM超越了现有的道空间注意力模块.
结论:
- 序列通道空间注意模块 (SCSAM) 为CNN提供了效率和计算复杂性之间的最佳平衡.
- SCSAM 集成带来了图像分类性能的显著改善.
- 这项工作为深度学习模型中注意力机制的有效组合提供了宝贵的见解.
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