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Vehicle Re Identification On Veri 776

Metrics

Rank1

mAP

Results

Performance results of various models on this benchmark

			Paper Title	Repository
MBR4B-LAI (w/ RK)	-	92.1	Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification
RPTM	97.3	88.0	Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems
A Strong Baseline	-	87.1	A Strong Baseline for Vehicle Re-Identification
MBR4B-LAI (without re-ranking)	-	86.0	Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification
MBR4B (without re-ranking)	-	84.72	Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification
CLIP-ReID (without re-ranking)	-	84.5	CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels
VehicleNet	96.78	83.41	VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification
ProNet++ (ResNet50)	-	83.4	Rethinking Person Re-identification from a Projection-on-Prototypes Perspective	-
TransReID	-	82.3	TransReID: Transformer-based Object Re-Identification
CA-Jaccard	-	81.4	CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification
ANet	96.8	81.2	AttributeNet: Attribute Enhanced Vehicle Re-Identification	-
GiT	96.86	80.34	GiT: Graph Interactive Transformer for Vehicle Re-identification	-
HPGN	96.72	80.18	Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
MSINet (2.3M w/o RK)	-	78.8	MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID
CAL	-	74.3	Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
QD-DLF	-	61.83	Vehicle Re-identification Using Quadruple Directional Deep Learning Features	-
Cluster Contrast	86.2	40.8	Cluster Contrast for Unsupervised Person Re-Identification

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