融合注意力机制与改进ResNet50的服装图像属性预测方法  

A clothing image attribute prediction method integrating attentionmechanism and improved ResNet50

作　　者：游小荣[1,3] 李淑芳 邵红燕[1,3] YOU Xiaorong;LI Shufang;SHAO Hongyan(School of Intelligent Manufacturing,Changzhou Vocational Institute of Textile and Garment,Changzhou 213164,China;School of Intelligent Textiles and Materials,Changzhou Vocational Institute of Textile and Garment,Changzhou 213164,China;Jiangsu Research Center of Intelligent Manufacturing Technology for Carbon Fiber and Advanced Material,Changzhou 213164,China)

机构地区：[1]常州纺织服装职业技术学院智能制造学院,江苏常州213164 [2]常州纺织服装职业技术学院智能纺织与材料学院,江苏常州213164 [3]江苏省碳纤维先进材料智能制造工程技术研究开发中心,江苏常州213164

出　　处：《现代纺织技术》2025年第1期58-64,共7页Advanced Textile Technology

基　　金：常州纺织服装职业技术学院2023年院学术科研基金项目(应用技术类)(CFK202316)。

摘　　要：为了解决人工标注服装图像属性效率低下的问题,提出了一种融合注意力机制与改进ResNet50的服装图像属性预测方法。首先对传统多标签分类方法中的模型进行了改进,改进后的方法能更充分利用任务之间的相关性,并减少数据稀缺问题带来的影响;接着引入CBAM注意力机制,用于捕捉服装属性上的细节特征。结果表明:在未引入注意力机制的情况下,基于改进ResNet50的方法在多项评价指标上均优于传统多标签分类方法,准确率提高了25.96%;与ResNet34、EfficientNet_V2、VGG16模型相比,ResNet50模型在服装图像属性预测方面整体表现更佳;引入CBAM注意力机制后,基于改进ResNet50的方法的准确率再提高了1.72%。所提的融合注意力机制与改进ResNet50的服装图像属性预测方法,能够有效预测服装图像属性,为实现服装图像属性的自动化标注提供了新的思路。In recent years,with the popularity of online shopping,a large number of clothing images have emerged on the Internet.How to automatically extract key information from these massive clothing images has become a hot topic in current research.Through analyzing and identifying the relevant attributes of these clothing images and combining them with information such as price,sales volume and user comments,intelligent recommendations and trend predictions can be further achieved.This not only helps merchants grasp market demand in advance and formulate more accurate marketing strategies and business decisions but also provides designers with valuable creative inspiration.However,labeling the attributes of a large number of clothing images is also a tedious and costly task for online clothing sellers.Therefore,researching the classification and prediction of clothing image attributes has important practical significance and application value.To improve the prediction accuracy of clothing image attributes and to address the inefficiency of manual labeling of clothing image attributes,this paper proposes a clothing image attribute prediction method integrating the attention mechanism and improved ResNet50.This method improves the network structure of the ResNet50 model to adapt to the clothing multi-attribute prediction task and introduces the attention mechanism into the improved ResNet50 model to capture the detailed features of clothing attributes to improve the prediction accuracy.The method not only applies the improved deep learning algorithm to clothing attribute prediction,but also verifies the effectiveness of the method in clothing attribute prediction.It can effectively improve the accuracy of clothing image attribute prediction and identify attribute categories with superior prediction outcomes,providing new ideas for realizing the automatic labeling of clothing image attributes.The experimental results show that in the absence of the attention mechanism,the method based on the improved ResNet50 outperforms t

关 键 词：服装图像 属性预测 注意力机制 ResNet50 深度学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TS102.3[自动化与计算机技术—计算机科学与技术]