Article(id=1309640090905830354, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250449, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1763913600000, revisedDateStr=2025-11-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172129607, onlineDateStr=2026-09-23, pubDate=1776960000000, pubDateStr=2026-04-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172129607, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172129607, creator=13701087609, updateTime=1790172129607, updator=13701087609, issue=Issue{id=1309640086191432626, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='4', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1776960000000', pubDateStr='2026-04-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172128484, creator='13701087609', updateTime=1790232864374, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894831258825016, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894831258825017, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=17, endPage=25, ext={EN=ArticleExt(id=1309640091065213907, articleId=1309640090905830354, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Vehicle Re-Identification Method Based on Adaptive Local Partitioning Vision Transformer, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address insufficient feature representation and poor robustness caused by viewpoint changes, occlusions, and visual similarity, this paper proposes an adaptive local partitioning Vision Transformer for vehicle re-identification. Firstly, an Adaptive Local Partitioning (ALP) module is designed to perform dynamic regional division. Secondly, by integrating low-level visual details with high-level global semantics through a Hierarchical Attention Fusion (HAF) module, it provides feature guidance for adaptive region segmentation. Finally, a Multi-Feature Embedding (MFE) module is introduced to enhance discriminability using a dynamic weighting mechanism for camera and viewpoint information. The experimental results show that in the vehicle re-identification task, the proposed method achieved an mAP of 81.0% and a Rank-1 of 97.1% on the VeRi-776 dataset, and a Rank-1 of 80.2% on the VehicleID dataset, significantly improving the model's identification accuracy and robustness.

, authors=Mengxi Xu¹, Haixin Chen², Huanyu Xu³, authorsList=Mengxi Xu, Haixin Chen, Huanyu Xu, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309640091253957588, articleId=1309640090905830354, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于自适应局部划分Vision Transformer的车辆重识别方法, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对复杂场景下因摄像头视角变化、遮挡及车辆外观相似性导致模型的特征表达不充分和鲁棒性不足等问题，提出一种基于自适应局部划分Vision Transformer的车辆重识别方法。结合卷积和注意力设计自适应局部划分（ALP）模块；通过层次化注意力融合（HAF）模块整合低层视觉细节与高层全局语义，为自适应区域划分提供特征引导；引入多重特征嵌入（MFE）模块，基于相机和视角的动态加权机制提升多视角、多相机环境下的特征区分能力。试验结果表明：在车辆重识别任务中，所提出方法在VeRi-776数据集上的mAP和Rank-1分别达到81.0%、97.1%，在VehicleID数据集上的Rank-1达到80.2%，显著提升了模型的识别精度和鲁棒性。

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