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Vehicle Re Identification On Veri 776

Metrics

Rank1
mAP

Results

Performance results of various models on this benchmark

Paper Title
MBR4B-LAI (w/ RK)-92.1Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification
RPTM97.388.0Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems
A Strong Baseline-87.1A Strong Baseline for Vehicle Re-Identification
MBR4B-LAI (without re-ranking)-86.0Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification
MBR4B (without re-ranking)-84.72Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification
CLIP-ReID (without re-ranking)-84.5CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels
VehicleNet96.7883.41VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification
ProNet++ (ResNet50)-83.4Rethinking Person Re-identification from a Projection-on-Prototypes Perspective
TransReID-82.3TransReID: Transformer-based Object Re-Identification
CA-Jaccard-81.4CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification
ANet96.881.2AttributeNet: Attribute Enhanced Vehicle Re-Identification
GiT96.8680.34GiT: Graph Interactive Transformer for Vehicle Re-identification
HPGN96.7280.18Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
MSINet (2.3M w/o RK)-78.8MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID
CAL-74.3Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
QD-DLF-61.83Vehicle Re-identification Using Quadruple Directional Deep Learning Features
Cluster Contrast86.240.8Cluster Contrast for Unsupervised Person Re-Identification
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Vehicle Re Identification On Veri 776 | SOTA | HyperAI