用于图像文本检索的跨模态独立匹配网络

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiao Ke , Baitao Chen , Xiong Yang , Yuhang Cai , Hao Liu , Wenzhong Guo
{"title":"用于图像文本检索的跨模态独立匹配网络","authors":"Xiao Ke ,&nbsp;Baitao Chen ,&nbsp;Xiong Yang ,&nbsp;Yuhang Cai ,&nbsp;Hao Liu ,&nbsp;Wenzhong Guo","doi":"10.1016/j.patcog.2024.111096","DOIUrl":null,"url":null,"abstract":"<div><div>Image-text retrieval serves as a bridge connecting vision and language. Mainstream modal cross matching methods can effectively perform cross-modal interactions with high theoretical performance. However, there is a deficiency in efficiency. Modal independent matching methods exhibit superior efficiency but lack in performance. Therefore, achieving a balance between matching efficiency and performance becomes a challenge in the field of image-text retrieval. In this paper, we propose a new Cross-modal Independent Matching Network (CIMN) for image-text retrieval. Specifically, we first use the proposed Feature Relationship Reasoning (FRR) to infer neighborhood and potential relations of modal features. Then, we introduce Graph Pooling (GP) based on graph convolutional networks to perform modal global semantic aggregation. Finally, we introduce the Gravitation Loss (GL) by incorporating sample mass into the learning process. This loss can correct the matching relationship between and within each modality, avoiding the problem of equal treatment of all samples in the traditional triplet loss. Extensive experiments on Flickr30K and MSCOCO datasets demonstrate the superiority of the proposed method. It achieves a good balance between matching efficiency and performance, surpasses other similar independent matching methods in performance, and can obtain retrieval accuracy comparable to some mainstream cross matching methods with an order of magnitude lower inference time.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cross-modal independent matching network for image-text retrieval\",\"authors\":\"Xiao Ke ,&nbsp;Baitao Chen ,&nbsp;Xiong Yang ,&nbsp;Yuhang Cai ,&nbsp;Hao Liu ,&nbsp;Wenzhong Guo\",\"doi\":\"10.1016/j.patcog.2024.111096\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Image-text retrieval serves as a bridge connecting vision and language. Mainstream modal cross matching methods can effectively perform cross-modal interactions with high theoretical performance. However, there is a deficiency in efficiency. Modal independent matching methods exhibit superior efficiency but lack in performance. Therefore, achieving a balance between matching efficiency and performance becomes a challenge in the field of image-text retrieval. In this paper, we propose a new Cross-modal Independent Matching Network (CIMN) for image-text retrieval. Specifically, we first use the proposed Feature Relationship Reasoning (FRR) to infer neighborhood and potential relations of modal features. Then, we introduce Graph Pooling (GP) based on graph convolutional networks to perform modal global semantic aggregation. Finally, we introduce the Gravitation Loss (GL) by incorporating sample mass into the learning process. This loss can correct the matching relationship between and within each modality, avoiding the problem of equal treatment of all samples in the traditional triplet loss. Extensive experiments on Flickr30K and MSCOCO datasets demonstrate the superiority of the proposed method. It achieves a good balance between matching efficiency and performance, surpasses other similar independent matching methods in performance, and can obtain retrieval accuracy comparable to some mainstream cross matching methods with an order of magnitude lower inference time.</div></div>\",\"PeriodicalId\":49713,\"journal\":{\"name\":\"Pattern Recognition\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pattern Recognition\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0031320324008471\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0031320324008471","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

图像-文本检索是连接视觉和语言的桥梁。主流的模态交叉匹配方法可以有效地进行跨模态交互,并具有较高的理论性能。但在效率方面存在不足。独立模态匹配方法效率高,但性能不足。因此,如何在匹配效率和性能之间取得平衡成为图像-文本检索领域的一项挑战。本文提出了一种用于图像文本检索的新型跨模态独立匹配网络(CIMN)。具体来说,我们首先使用提出的特征关系推理(FRR)来推断模态特征的邻域和潜在关系。然后,我们引入基于图卷积网络的图池化(GP)来执行模态全局语义聚合。最后,我们将样本质量纳入学习过程,引入引力损失(GL)。这种损失可以纠正每种模态之间和内部的匹配关系,避免了传统三重损失中平等对待所有样本的问题。在 Flickr30K 和 MSCOCO 数据集上进行的大量实验证明了所提出方法的优越性。它在匹配效率和性能之间实现了良好的平衡,在性能上超越了其他类似的独立匹配方法,并能获得与一些主流交叉匹配方法相当的检索精度,推理时间却低了一个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-modal independent matching network for image-text retrieval
Image-text retrieval serves as a bridge connecting vision and language. Mainstream modal cross matching methods can effectively perform cross-modal interactions with high theoretical performance. However, there is a deficiency in efficiency. Modal independent matching methods exhibit superior efficiency but lack in performance. Therefore, achieving a balance between matching efficiency and performance becomes a challenge in the field of image-text retrieval. In this paper, we propose a new Cross-modal Independent Matching Network (CIMN) for image-text retrieval. Specifically, we first use the proposed Feature Relationship Reasoning (FRR) to infer neighborhood and potential relations of modal features. Then, we introduce Graph Pooling (GP) based on graph convolutional networks to perform modal global semantic aggregation. Finally, we introduce the Gravitation Loss (GL) by incorporating sample mass into the learning process. This loss can correct the matching relationship between and within each modality, avoiding the problem of equal treatment of all samples in the traditional triplet loss. Extensive experiments on Flickr30K and MSCOCO datasets demonstrate the superiority of the proposed method. It achieves a good balance between matching efficiency and performance, surpasses other similar independent matching methods in performance, and can obtain retrieval accuracy comparable to some mainstream cross matching methods with an order of magnitude lower inference time.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信