{"title":"用于人员再识别的动态注意力视觉语言转换器网络","authors":"Guifang Zhang, Shijun Tan, Zhe Ji, Yuming Fang","doi":"10.1007/s11263-024-02277-3","DOIUrl":null,"url":null,"abstract":"<p>Multimodal based person re-identification (ReID) has garnered increasing attention in recent years. However, the integration of visual and textual information encounters significant challenges. Biases in feature integration are frequently observed in existing methods, resulting in suboptimal performance and restricted generalization across a spectrum of ReID tasks. At the same time, since there is a domain gap between the datasets used by the pretraining model and the ReID datasets, it has a certain impact on the performance. In response to these challenges, we proposed a dynamic attention vision-language transformer network for the ReID task. In this network, a novel image-text dynamic attention module (ITDA) is designed to promote unbiased feature integration by dynamically assigning the importance of image and text representations. Additionally, an adapter module is adopted to address the domain gap between pretraining datasets and ReID datasets. Our network can capture complex connections between visual and textual information and achieve satisfactory performance. We conducted numerous experiments on ReID benchmarks to demonstrate the efficacy of our proposed method. The experimental results show that our method achieves state-of-the-art performance, surpassing existing integration strategies. These findings underscore the critical role of unbiased feature dynamic integration in enhancing the capabilities of multimodal based ReID models.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"96 1","pages":""},"PeriodicalIF":11.6000,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic Attention Vision-Language Transformer Network for Person Re-identification\",\"authors\":\"Guifang Zhang, Shijun Tan, Zhe Ji, Yuming Fang\",\"doi\":\"10.1007/s11263-024-02277-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Multimodal based person re-identification (ReID) has garnered increasing attention in recent years. However, the integration of visual and textual information encounters significant challenges. Biases in feature integration are frequently observed in existing methods, resulting in suboptimal performance and restricted generalization across a spectrum of ReID tasks. At the same time, since there is a domain gap between the datasets used by the pretraining model and the ReID datasets, it has a certain impact on the performance. In response to these challenges, we proposed a dynamic attention vision-language transformer network for the ReID task. In this network, a novel image-text dynamic attention module (ITDA) is designed to promote unbiased feature integration by dynamically assigning the importance of image and text representations. Additionally, an adapter module is adopted to address the domain gap between pretraining datasets and ReID datasets. Our network can capture complex connections between visual and textual information and achieve satisfactory performance. We conducted numerous experiments on ReID benchmarks to demonstrate the efficacy of our proposed method. The experimental results show that our method achieves state-of-the-art performance, surpassing existing integration strategies. These findings underscore the critical role of unbiased feature dynamic integration in enhancing the capabilities of multimodal based ReID models.</p>\",\"PeriodicalId\":13752,\"journal\":{\"name\":\"International Journal of Computer Vision\",\"volume\":\"96 1\",\"pages\":\"\"},\"PeriodicalIF\":11.6000,\"publicationDate\":\"2024-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computer Vision\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s11263-024-02277-3\",\"RegionNum\":2,\"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":"International Journal of Computer Vision","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11263-024-02277-3","RegionNum":2,"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}
Dynamic Attention Vision-Language Transformer Network for Person Re-identification
Multimodal based person re-identification (ReID) has garnered increasing attention in recent years. However, the integration of visual and textual information encounters significant challenges. Biases in feature integration are frequently observed in existing methods, resulting in suboptimal performance and restricted generalization across a spectrum of ReID tasks. At the same time, since there is a domain gap between the datasets used by the pretraining model and the ReID datasets, it has a certain impact on the performance. In response to these challenges, we proposed a dynamic attention vision-language transformer network for the ReID task. In this network, a novel image-text dynamic attention module (ITDA) is designed to promote unbiased feature integration by dynamically assigning the importance of image and text representations. Additionally, an adapter module is adopted to address the domain gap between pretraining datasets and ReID datasets. Our network can capture complex connections between visual and textual information and achieve satisfactory performance. We conducted numerous experiments on ReID benchmarks to demonstrate the efficacy of our proposed method. The experimental results show that our method achieves state-of-the-art performance, surpassing existing integration strategies. These findings underscore the critical role of unbiased feature dynamic integration in enhancing the capabilities of multimodal based ReID models.
期刊介绍:
The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs.
Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision.
Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community.
Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas.
In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives.
The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research.
Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.