{"title":"用于可见光-红外线人员再识别的自适应中间模态对齐学习","authors":"Yukang Zhang, Yan Yan, Yang Lu, Hanzi Wang","doi":"10.1007/s11263-024-02276-4","DOIUrl":null,"url":null,"abstract":"<p>Visible-infrared person re-identification (VIReID) has attracted increasing attention due to the requirements for 24-hour intelligent surveillance systems. In this task, one of the major challenges is the modality discrepancy between the visible (VIS) and infrared (NIR) images. Most conventional methods try to design complex networks or generative models to mitigate the cross-modality discrepancy while ignoring the fact that the modality gaps differ between the different VIS and NIR images. Different from existing methods, in this paper, we propose an Adaptive Middle-modality Alignment Learning (AMML) method, which can effectively reduce the modality discrepancy via an adaptive middle modality learning strategy at both image level and feature level. The proposed AMML method enjoys several merits. First, we propose an Adaptive Middle-modality Generator (AMG) module to reduce the modality discrepancy between the VIS and NIR images from the image level, which can effectively project the VIS and NIR images into a unified middle modality image (UMMI) space to adaptively generate middle-modality (M-modality) images. Second, we propose a feature-level Adaptive Distribution Alignment (ADA) loss to force the distribution of the VIS features and NIR features adaptively align with the distribution of M-modality features. Moreover, we also propose a novel Center-based Diverse Distribution Learning (CDDL) loss, which can effectively learn diverse cross-modality knowledge from different modalities while reducing the modality discrepancy between the VIS and NIR modalities. Extensive experiments on three challenging VIReID datasets show the superiority of the proposed AMML method over the other state-of-the-art methods. More remarkably, our method achieves 77.8% in terms of Rank-1 and 74.8% in terms of mAP on the SYSU-MM01 dataset for all search mode, and 86.6% in terms of Rank-1 and 88.3% in terms of mAP on the SYSU-MM01 dataset for indoor search mode. The code is released at: https://github.com/ZYK100/MMN.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"24 1","pages":""},"PeriodicalIF":11.6000,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive Middle Modality Alignment Learning for Visible-Infrared Person Re-identification\",\"authors\":\"Yukang Zhang, Yan Yan, Yang Lu, Hanzi Wang\",\"doi\":\"10.1007/s11263-024-02276-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Visible-infrared person re-identification (VIReID) has attracted increasing attention due to the requirements for 24-hour intelligent surveillance systems. In this task, one of the major challenges is the modality discrepancy between the visible (VIS) and infrared (NIR) images. Most conventional methods try to design complex networks or generative models to mitigate the cross-modality discrepancy while ignoring the fact that the modality gaps differ between the different VIS and NIR images. Different from existing methods, in this paper, we propose an Adaptive Middle-modality Alignment Learning (AMML) method, which can effectively reduce the modality discrepancy via an adaptive middle modality learning strategy at both image level and feature level. The proposed AMML method enjoys several merits. First, we propose an Adaptive Middle-modality Generator (AMG) module to reduce the modality discrepancy between the VIS and NIR images from the image level, which can effectively project the VIS and NIR images into a unified middle modality image (UMMI) space to adaptively generate middle-modality (M-modality) images. Second, we propose a feature-level Adaptive Distribution Alignment (ADA) loss to force the distribution of the VIS features and NIR features adaptively align with the distribution of M-modality features. Moreover, we also propose a novel Center-based Diverse Distribution Learning (CDDL) loss, which can effectively learn diverse cross-modality knowledge from different modalities while reducing the modality discrepancy between the VIS and NIR modalities. Extensive experiments on three challenging VIReID datasets show the superiority of the proposed AMML method over the other state-of-the-art methods. More remarkably, our method achieves 77.8% in terms of Rank-1 and 74.8% in terms of mAP on the SYSU-MM01 dataset for all search mode, and 86.6% in terms of Rank-1 and 88.3% in terms of mAP on the SYSU-MM01 dataset for indoor search mode. The code is released at: https://github.com/ZYK100/MMN.</p>\",\"PeriodicalId\":13752,\"journal\":{\"name\":\"International Journal of Computer Vision\",\"volume\":\"24 1\",\"pages\":\"\"},\"PeriodicalIF\":11.6000,\"publicationDate\":\"2024-11-09\",\"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/s11263-024-02276-4\",\"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/s11263-024-02276-4","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}
Adaptive Middle Modality Alignment Learning for Visible-Infrared Person Re-identification
Visible-infrared person re-identification (VIReID) has attracted increasing attention due to the requirements for 24-hour intelligent surveillance systems. In this task, one of the major challenges is the modality discrepancy between the visible (VIS) and infrared (NIR) images. Most conventional methods try to design complex networks or generative models to mitigate the cross-modality discrepancy while ignoring the fact that the modality gaps differ between the different VIS and NIR images. Different from existing methods, in this paper, we propose an Adaptive Middle-modality Alignment Learning (AMML) method, which can effectively reduce the modality discrepancy via an adaptive middle modality learning strategy at both image level and feature level. The proposed AMML method enjoys several merits. First, we propose an Adaptive Middle-modality Generator (AMG) module to reduce the modality discrepancy between the VIS and NIR images from the image level, which can effectively project the VIS and NIR images into a unified middle modality image (UMMI) space to adaptively generate middle-modality (M-modality) images. Second, we propose a feature-level Adaptive Distribution Alignment (ADA) loss to force the distribution of the VIS features and NIR features adaptively align with the distribution of M-modality features. Moreover, we also propose a novel Center-based Diverse Distribution Learning (CDDL) loss, which can effectively learn diverse cross-modality knowledge from different modalities while reducing the modality discrepancy between the VIS and NIR modalities. Extensive experiments on three challenging VIReID datasets show the superiority of the proposed AMML method over the other state-of-the-art methods. More remarkably, our method achieves 77.8% in terms of Rank-1 and 74.8% in terms of mAP on the SYSU-MM01 dataset for all search mode, and 86.6% in terms of Rank-1 and 88.3% in terms of mAP on the SYSU-MM01 dataset for indoor search mode. The code is released at: https://github.com/ZYK100/MMN.
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.