Narrow Pooling Clothing Classification Based on Attention Mechanism

作     者：马骁 王绍宇 叶少萍 樊静宜 徐安 夏小玲 MA Xiao;WANG Shaoyu;YE Shaoping;FAN Jingyi;XU An;XIA Xiaoling

作者机构：School of Computer Science and TechnologyDonghua UniversityShanghai 201620China 

出 版 物：《Journal of Donghua University(English Edition)》 (东华大学学报（英文版）)

年 卷 期：2022年第39卷第4期

页      面：367-372页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081202[工学-计算机软件与理论] 

主　　题：clothing classification convolutional neural network(CNN) residual network(ResNet) attention mechanism narrow pooling 

摘      要：In recent years,with the rapid development of e-commerce,people need to classify the wide variety and a large number of clothing images appearing on e-commerce platforms.In order to solve the problems of long time consumption and unsatisfactory classification accuracy arising from the classification of a large number of clothing images,researchers have begun to exploit deep learning techniques instead of traditional learning methods.The paper explores the use of convolutional neural networks(CNNs)for feature learning to enhance global feature information interactions by adding an improved hybrid attention mechanism(HAM)that fully utilizes feature weights in three dimensions:channel,height,and width.Moreover,the improved pooling layer not only captures local feature information,but also fuses global and local information to improve the misclassification problem that occurs between similar categories.Experiments on the Fashion-MNIST and DeepFashion datasets show that the proposed method significantly improves the accuracy of clothing classification(93.62%and 67.9%)compared with residual network(ResNet)and convolutional block attention module(CBAM).