[1]行阳阳,张索非,宋 越,等.一种面向商品检索的多尺度度量学习方法[J].计算机技术与发展,2024,34(01):65-70.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 010]
　XING Yang-yang,ZHANG Suo-fei,SONG Yue,et al.A Multi-scale Metric Learning Approach for Product Retrieval[J].,2024,34(01):65-70.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 010]
点击复制

一种面向商品检索的多尺度度量学习方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年01期
页码:
65-70
栏目:
媒体计算
出版日期:
2024-01-10

文章信息/Info

Title:
A Multi-scale Metric Learning Approach for Product Retrieval
文章编号:
1673-629X(2024)01-0065-06
作者:
行阳阳1 ; 张索非1 ; 宋 越2 ; 吴晓富1 ; 周 全1
1. 南京邮电大学,江苏 南京 210003;
2. 95958 部队,上海 200120
Author(s):
XING Yang-yang1 ; ZHANG Suo-fei1 ; SONG Yue2 ; WU Xiao-fu1 ; ZHOU Quan1
1. Nanjing University of Posts and Telecommunications,Nanjing 210003,China;
2. 95958 Troop,Shanghai 200120,China
关键词:
度量学习; 商品识别; 多尺度; 图像检索; 特征融合
Keywords:
metric learning; product identification; multiple scale; image retrieval; feature fusion
分类号:
TP31
DOI:
10. 3969 / j. issn. 1673-629X. 2024. 01. 010
摘要:
商品图像检索是一个典型的大规模度量学习任务,其特点在于商品零售平台需要定期上架新类型的商品,且同一类型的商品外观会不时发生变化。 已有的工作表明:传统基于单一的度量学习虽然可以将商品检索模型的识别范围扩展到未知商品类别上,但是其性能仍然受限。 为此,提出了一种基于多尺度监督信息的深度度量学习商品检索方法。 该方法利用商品多个尺度的标签信息训练并使用协同注意力机制对不同尺度的深度特征进行有效融合,提高了深度学习模型挖掘重要信息的能力,从而有效提高了其在细粒度级别下的检索性能。 在大规模商品检索数据集上的实验结果表明,该方法在 mAP 和 Rank-1 上分别为 43. 0% 和 65. 9% 。 相比于传统度量学习方法分别提升了 6. 4% 和 7. 8% 。
Abstract:
Product image retrieval is a typical large-scale metric learning task. The specificity of this task is that the commodity retailplatform needs to import new types of items regularly, and the appearances of existing products also change from time to time. Theprevious work show that although the traditional metric learning can extend the recognition range of product retrieval to unseen producttypes,the performance of traditional metric learning in product retrieval is still limited,because it can only use a single scale of regulatory　information to deal with such large-scale retrieval problems. Therefore,we propose a product retrieval method based on multi-scale deepmetric learning. The proposed method uses the label information of multiple scales to train the model and adopts the co-attention moduleto integrate the deep features of different scales effectively,which can improve the ability of the model to obtain important informationand effectively improve the retrieval performance of the deep learning model at the fine-grained level. The proposed method achieves43. 0% and 65. 9% on mAP and Rank - 1 in experiments on large - scale product retrieval dataset, improved by 6. 4% and 7. 8% ,respectively compared with the traditional metric learning.

相似文献/References:

[1]王晴[],荆晓远[][],朱阳平[],等. 基于局部稀疏重构度量学习的软件缺陷预测[J].计算机技术与发展,2016,26(11):54.
　WANG Qing[],JING Xiao-yuan[][],ZHU Yang-ping[],et al. Software Defect Prediction of Metric Learning Based on Local Sparse Reconstruction[J].,2016,26(01):54.
[2]史作婷,吴 迪,荆晓远,等.类不平衡稀疏重构度量学习软件缺陷预测[J].计算机技术与发展,2018,28(06):125.[doi:10.3969/ j. issn.1673-629X.2018.06.028]
　SHI Zuo-ting,WU Di,JING Xiao-yuan,et al.Prediction of Defect of Class-imbalance Sparse Reconstruction Metric Learning Software[J].,2018,28(01):125.[doi:10.3969/ j. issn.1673-629X.2018.06.028]
[3]刘 军,孙 庆,刘 玮,等.基于 U-Net 网络和椭圆度量学习的防震锤锈蚀识别[J].计算机技术与发展,2020,30(11):163.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 030]
　LIU Jun,SUN Qing,LIU Wei,et al.Identification of Anti-vibration Hammer Corrosion of High-voltage Transmission Lines Based on U-Net Network and Elliptic Metric Learning[J].,2020,30(01):163.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 030]
[4]刘 东,胡峻林*.基于度量学习的步态识别比较研究[J].计算机技术与发展,2022,32(09):89.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 014]
　LIU Dong,HU Jun-lin *.Comparative Study of Gait Recognition Based on Metric Learning[J].,2022,32(01):89.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 014]
[5]江雨燕,吕 魏,李 平,等.基于无监督的非对称度量学习优化行人再识别[J].计算机技术与发展,2022,32(09):126.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 020]
　JIANG Yu-yan,LYU Wei,LI Ping,et al.Unsupervised Asymmetric Metric Learning for Person Re-identification Optimization[J].,2022,32(01):126.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 020]

更新日期/Last Update: 2024-01-10