Vehicle Re-Identification

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9 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Learning Discriminative Prototypes via Boundary Repulsion MixUp for Object Re-Identification
Oct 25, 2026Athena Psalta, Vasileios Tsironis, Konstantinos KarantzalosVehicle Re-Identification Person Re-Identification
National Technical University of Athens, Iroon Polytechneiou 9, Athens 15780, Greece
Front-to-Back: Benchmarking Vision-Language Models for Asymmetric Cross-View Vehicle Re-Identification
Sep 30, 2026Moseli Mots'oehli, Thulani BabeliVLM Evaluation Image Matching
MindForge AI, Johannesburg, South Africa · University of Hawai’i at Mānoa, Honolulu, HI, USA
Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification under Foundation-Model Pretraining
Jul 24, 2026Yu Wang, Hongyu YangVision Foundation Models Hybrid CNN-Transformer Architectures
Huahuan (Yunnan) Technology Co., Ltd., Yunnan, China
Benchmarking UAV-based Vehicle Re-Identification under Simulated Weather Conditions
Jul 12, 2026Vu Minh Tran, Khang NguyenDistribution Shift Robustness Vehicle Re-Identification
University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh City, Vietnam
Mixture of Enhanced-View Experts for Multi-Query Vehicle ReID and A Large-Scale Benchmark
Jul 9, 2026Aihua Zheng, Jie Zhen, Chenglong Li +2Multi-View Consistency Multi-View Learning
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Artificial Intelligence, Anhui University. Hefei, 230601, China · Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Artificial Intelligence, Anhui University. Hefei, 230601, China · School of Artificial Intelligence, Anhui University of Science and Technology, Hefei 232001, China +1
Zero-Shot Semantic Re-Identification for Autonomous Driving: A VLM Baseline Study
Jun 8, 2026Eduardo Borges, Manuel Abreu, Luís Garrote +1VLM Evaluation Vision-Language Models
Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal
Generalization Limits in Vehicle Re-Identification
Jun 1, 2026Anis Yassine Ben Mabrouk, Antoine Tadros, Rafael Grompone von Gioi +3Vehicle Re-Identification Fine-Grained Visual Perception
CB · Universit´e Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli · CMLA, LIGM +3
T2I-VeRW: Part-level Fine-grained Perception for Text-to-Image Vehicle Retrieval
May 7, 2026Xiao Wang, Ziwen Wang, Weizhe Kong +5Fine-Grained Image Retrieval Cross-Modal Retrieval
School of Computer Science and Technology, Anhui University, Hefei 230601, China · School of Artificial Intelligence, Anhui University, Hefei 230601, China
VC-FeS: Viewpoint-Conditioned Feature Selection for Vehicle Re-identification in Thermal Vision
May 6, 2026Yasod Ginige, Ransika Gunasekara, Darsha Hewavitharana +3Thermal Imaging Vehicle Re-Identification
aSchool of Computer Science, University of Sydney, NSW (2008), Australia · bDepartment of Electronics and Telecommunication Engineering, University of Moratuwa, Katubedda, Colombo (10400), Sri Lanka

Oct 25, 2026J/K move · Enter open · S save

Learning Discriminative Prototypes via Boundary Repulsion MixUp for Object Re-Identification

Athena Psalta, Vasileios Tsironis, Konstantinos Karantzalos

National Technical University of Athens, Iroon Polytechneiou 9, Athens 15780, Greece

Vehicle Re-Identification Person Re-Identification Prototype-Based Classification Mixup Query Re-Identification

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Object re-identification requires embeddings that are robust to intra-class variation in viewpoint, illumination, and occlusion, yet preserve sharp separation between visually similar identities. The hardest errors arise precisely where confusable identities meet, a region that pairwise losses shape only implicitly. We present PBR-ReID, a prototype-based framework that makes this inter-class geometry an explicit learning target. Each identity is represented by a learnable prototype jointly optimized with a DiNOv3 backbone and the resulting prototype similarity matrix reveals, at every training step, which identities are most confusable. On this signal we build Boundary Repulsion MixUp (BRM), a geometry-aware regularizer that converts standard MixUp's weakness into a discriminative signal. Rather than interpolating samples across confusable identities, which blurs the boundaries that matter most, BRM constructs an explicit boundary proxy between each confusable pair and repels their features away from it, converting boundary ambiguity into a discriminative signal. We adopt a two-stage training protocol consisting of a prototype warmup phase followed by EMA-stabilized joint fine-tuning. Extensive experiments on person and vehicle ReID benchmarks demonstrate strong performance, with thorough ablations confirming each component's contribution. Code is available at https://github.com/psaltaath/pbr-reid.