SPAM: Spatially Partitioned Attention Module in Deep Convolutional Neural Networks for Image Classification

WANG Fang; QIAO Ruiping

doi:10.7652/xjtuxb202309019

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SPAM: Spatially Partitioned Attention Module in Deep Convolutional Neural Networks for Image Classification

更新时间：2025-07-09

- SPAM: Spatially Partitioned Attention Module in Deep Convolutional Neural Networks for Image Classification
- Vol. 57, Issue 9, Pages: 185-192(2023)
- 作者机构：
  
  西安交通大学信息与通信工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202309019
  CLC： TP391.4
- Online First：10 September 2023，
  
  Published：2023
- 稿件说明：
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WANG Fang, QIAO Ruiping. SPAM: Spatially Partitioned Attention Module in Deep Convolutional Neural Networks for Image Classification[J]. 2023, 57(9): 185-192.
DOI：

WANG Fang, QIAO Ruiping. SPAM: Spatially Partitioned Attention Module in Deep Convolutional Neural Networks for Image Classification[J]. 2023, 57(9): 185-192. DOI： 10.7652/xjtuxb202309019.

摘要

针对现有注意力机制常采用融合或压缩的方式来获取所需信息

会导致空间或通道维度损失的信息过多的问题

提出了一种有效的轻量级注意力模块SPAM

它可以在不经过通道融合或压缩的情况下获取注意力。对于输入的中间特征图

SPAM首先会自适应地采用平均池化和最大池化来进行特征提取; 采用空间上局部的块特征代替点特征以减少计算量

并利用实例标准化(IN)层与深层卷积来捕获全局空间注意力; 通道维度信息的重建通过分组卷积来完成; 最终使用插值操作获得整体注意力

对输入特征图进行加权。SPAM可以方便地嵌入到各种主流卷积神经网络架构中

只需增加微量参数和计算量

就可以显著提高网络性能。为了证明SPAM的有效性

在ImageNet-1K、CIFAR-100和Food-101图像分类数据集上进行了大量实验

并使用Grad-CAM可视化了网络的关注区域。实验结果表明

在ImageNet-1K、CIFAR-100和Food-101数据集上

SPAM分别将基线网络的准确率最多提高了约1.08%、2.46%和1.09%。研究结果表明

嵌入SPAM的网络的性能都有较大提升; 且相较于其他常用的轻量级注意力机制

SPAM的效果始终更好; SPAM使网络更关注目标对象所在区域

确切提高了网络的表达能力。

Abstract

Existing attention mechanisms often use fusion or compression to obtain the required information

but this leads to a large quantity of information lost in the spatial or channel dimension. In order to solve this problem

the Spatially Partitioned Attention Module(SPAM)

a really effective and lightweight attention module that can help obtain attention without channel fusion or compression

was proposed in the paper. For the input intermediate feature map

the SPAM first adaptively used average pooling and maximum pooling features for feature extraction

replaced the point feature with the local block feature in space to reduce the amount of calculation and used the IN layer and depthwise convolution to capture global spatial attention. Meanwhile

the reconstruction of channel dimension information was directly completed by group convolution. Finally

the interpolation operation was used to obtain overall attention and weight the input feature map. Notably

the SPAM can be easily embedded in various mainstream CNN architectures

and network performance can be significantly improved by increasing a few microparameters and calculations. To demonstrate the effectiveness of the SPAM

numerous experiments were conducted on the ImageNet-1K

CIFAR-100

and Food-101 datasets

and the network's regions of interest were visualized using Grad-CAM. On the ImageNet-1K

CIFAR-100

and Food-101 datasets

the SPAM improved the accuracy of the baseline network by up to about 1.08%

2.46%

and 1.09%

respectively. The results show that the performance of the network embedded with the SPAM components is greatly improved; compared to other commonly used lightweight attention mechanisms

the SPAM always works better; the SPAM can really induce the networks to pay more attention to the target object regions and accurately improve the expression ability of the networks.

关键词

Keywords

references

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