{"title":"Cross-modal independent matching network for image-text retrieval","authors":"","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}
引用次数: 0
Abstract
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.
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.