SOTAVerified

Vehicle Re-Identification

Vehicle re-identification is the task of identifying the same vehicle across multiple cameras.

( Image credit: A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Cameras )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 150 papers

TitleStatusHype
Part-level Car Parsing and Reconstruction from Single Street View—0
Part-Regularized Near-Duplicate Vehicle Re-Identification—0
PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks—0
Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identification—0
Progressive learning with multi-scale attention network for cross-domain vehicle re-identification—0
RAM: A Region-Aware Deep Model for Vehicle Re-Identification—0
Revisiting Multi-Granularity Representation via Group Contrastive Learning for Unsupervised Vehicle Re-identification—0
Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking and Vehicle Re-ID in Multi-Camera Networks—0
Scalable Vehicle Re-Identification via Self-Supervision—0
Self-aligned Spatial Feature Extraction Network for UAV Vehicle Re-identification—0
Rethinking Person Re-identification from a Projection-on-Prototypes Perspective—0
A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges—0
A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance—0
A Novel Dual-pooling Attention Module for UAV Vehicle Re-identification—0
A survey of advances in vision-based vehicle re-identification—0
Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification—0
Attribute-guided Feature Learning Network for Vehicle Re-identification—0
AttributeNet: Attribute Enhanced Vehicle Re-Identification—0
Attributes Guided Feature Learning for Vehicle Re-identification—0
A unified neural network for object detection, multiple object tracking and vehicle re-identification—0
Background Segmentation for Vehicle Re-Identification—0
CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification—0
CLIP-SENet: CLIP-based Semantic Enhancement Network for Vehicle Re-identification—0
Cluster images with AntClust: a clustering algorithm based on the chemical recognition system of ants—0
Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification—0
Complete Solution for Vehicle Re-ID in Surround-view Camera System—0
Context-Aware Graph Convolution Network for Target Re-identification—0
Cross Domain Knowledge Learning with Dual-branch Adversarial Network for Vehicle Re-identification—0
Cross Domain Knowledge Transfer for Unsupervised Vehicle Re-identification—0
Data Augmentation and Clustering for Vehicle Make/Model Classification—0
DCDLearn: Multi-order Deep Cross-distance Learning for Vehicle Re-Identification—0
Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles—0
Discriminative Feature and Dictionary Learning with Part-aware Model for Vehicle Re-identification—0
Discriminative Feature Representation with Spatio-temporal Cues for Vehicle Re-identification—0
Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification—0
DSAM: A Distance Shrinking with Angular Marginalizing Loss for High Performance Vehicle Re-identificatio—0
DSAM-GN:Graph Network based on Dynamic Similarity Adjacency Matrices for Vehicle Re-identification—0
Dual Embedding Expansion for Vehicle Re-identification—0
Dual-Level Viewpoint-Learning for Cross-Domain Vehicle Re-Identification—0
DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identification—0
Eliminating cross-camera bias for vehicle re-identification—0
Enhanced Vehicle Re-identification for ITS: A Feature Fusion approach using Deep Learning—0
GiT: Graph Interactive Transformer for Vehicle Re-identification—0
Global-Supervised Contrastive Loss and View-Aware-Based Post-Processing for Vehicle Re-Identification—0
Image-based Vehicle Re-identification Model with Adaptive Attention Modules and Metadata Re-ranking—0
Image-to-image domain adaptation for vehicle re-identification—0
LABNet: Local Graph Aggregation Network with Class Balanced Loss for Vehicle Re-Identification—0
Large-scale Fully-Unsupervised Re-Identification—0
Learning Canonical 3D Object Representation for Fine-Grained Recognition—0
Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals—0
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All datasetsVeRi-776VehicleID SmallVehicleID LargeVehicleID MediumVRAI test-devVeRi-Wild SmallVRAI testVeRiVeRi-Wild LargeCityFlowVehicleIDVeRi-Wild Medium

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MBR4B-LAI (w/ RK)mAP92.1—Unverified
2RPTMmAP88—Unverified
3A Strong BaselinemAP87.1—Unverified
4MBR4B-LAI (without re-ranking)mAP86—Unverified
5MBR4B (without re-ranking)mAP84.72—Unverified
6CLIP-ReID (without re-ranking)mAP84.5—Unverified
7VehicleNetmAP83.41—Unverified
8ProNet++ (ResNet50)mAP83.4—Unverified
9TransReIDmAP82.3—Unverified
10CA-JaccardmAP81.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Recall@k Surrogate loss (ViT-B/16)Rank-196.2—Unverified
2Recall@k Surrogate loss (ResNet-50)Rank-195.7—Unverified
3RPTMRank-195.5—Unverified
4PNP LossRank-195.5—Unverified
5Smooth-APRank-194.9—Unverified
6MBR-4B (without RK)Rank-188.3—Unverified
7ANetRank-187.9—Unverified
8CLIP-ReID (without re-ranking)Rank-185.5—Unverified
9GiTRank184.65—Unverified
10HPGNRank183.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Recall@k Surrogate loss (ViT-B/16)Rank-194.7—Unverified
2Recall@k Surrogate loss (ResNet-50)Rank-193.8—Unverified
3PNP LossRank-193.2—Unverified
4RPTMRank-192.9—Unverified
5Smooth-APRank-191.9—Unverified
6ANetRank-180.5—Unverified
7vehiclenetRank-179.46—Unverified
8MSINet (2.3M w/o RK)Rank-177.9—Unverified
9CALRank-175.1—Unverified
10QD-DLFmAP68.41—Unverified
#ModelMetricClaimedVerifiedStatus
1Recall@k Surrogate loss (ViT-B/16)Rank-195.2—Unverified
2Recall@k Surrogate loss (ResNet-50)Rank-194.6—Unverified
3PNP LossRank-194.2—Unverified
4RPTMRank-193.3—Unverified
5Smooth-APRank-193.3—Unverified
6ANetRank-182.8—Unverified
7vehiclenetRank-181.35—Unverified
8CALRank-178.2—Unverified
9QD-DLFmAP74.63—Unverified
#ModelMetricClaimedVerifiedStatus
1Baseline ModelMAP0.78—Unverified
2abu_0916MAP0.78—Unverified
3Juice LeeMAP0.7—Unverified
4nangggMAP0.63—Unverified
5asdfMAP0.62—Unverified
692MAP0.56—Unverified
#ModelMetricClaimedVerifiedStatus
1MBR-4B (without RK)Rank196.6—Unverified
2ANetRank196.5—Unverified
3ResNet50-IBNmAP73.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Baseline ModelMAP0.79—Unverified
2Juice LeeMAP0.69—Unverified
3nangggMAP0.62—Unverified
#ModelMetricClaimedVerifiedStatus
1vehiclenetmAP83.41—Unverified
2VKD (ResVKD-50)mAP82.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ANetmAP75.9—Unverified
2ResNet50-IBNmAP58.62—Unverified
#ModelMetricClaimedVerifiedStatus
1A Strong BaselinemAP61.34—Unverified
#ModelMetricClaimedVerifiedStatus
1VehicleNetRank183.64—Unverified
#ModelMetricClaimedVerifiedStatus
1ANetRank195.2—Unverified