SOTAVerified

Person Re-Identification

Person Re-Identification is a computer vision task in which the goal is to match a person's identity across different cameras or locations in a video or image sequence. It involves detecting and tracking a person and then using features such as appearance, body shape, and clothing to match their identity in different frames. The goal is to associate the same person across multiple non-overlapping camera views in a robust and efficient manner.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1226–1250 of 1488 papers

TitleStatusHype
Attention-based Few-Shot Person Re-identification Using Meta Learning—0
Deep Similarity Metric Learning for Real-Time Pedestrian Tracking—0
Convex Class Model on Symmetric Positive Definite Manifolds—0
Cross-dataset Person Re-Identification Using Similarity Preserved Generative Adversarial NetworksCode0
Semantically Selective Augmentation for Deep Compact Person Re-Identification—0
Person Re-Identification With Cascaded Pairwise Convolutions—0
Easy Identification From Better Constraints: Multi-Shot Person Re-Identification From Reference Constraints—0
Exploiting Transitivity for Learning Person Re-Identification Models on a Budget—0
Group Consistent Similarity Learning via Deep CRF for Person Re-Identification—0
Video Person Re-Identification With Competitive Snippet-Similarity Aggregation and Co-Attentive Snippet Embedding—0
Mask-Guided Contrastive Attention Model for Person Re-IdentificationCode0
Pose Transferrable Person Re-Identification—0
Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning—0
Adversarially Occluded Samples for Person Re-Identification—0
Eliminating Background-Bias for Robust Person Re-Identification—0
Multistage Adversarial Losses for Pose-Based Human Image Synthesis—0
Key Person Aided Re-identification in Partially Ordered Pedestrian Set—0
Resource Aware Person Re-identification across Multiple ResolutionsCode0
Density-Adaptive Kernel based Efficient Reranking Approaches for Person Reidentification—0
A Deep Structure of Person Re-Identification using Multi-Level Gaussian Models—0
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification—0
An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification—0
Robust and Efficient Graph Correspondence Transfer for Person Re-identification—0
Attention-Aware Compositional Network for Person Re-identification—0
Sharp Attention Network via Adaptive Sampling for Person Re-identification—0
Show:102550
← PrevPage 50 of 60Next →
All datasetsMarket-1501DukeMTMC-reIDMSMT17Occluded-DukeMTMCMarket-1501-CCUHK03 detectedCUHK03 labeledMARSCUHK03LTCCPRCCPRID2011

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1st-ReID(RE, RK)Rank-198—Unverified
2SSKD(GH)Rank-197.36—Unverified
3CLIP-ReID+Pose2ID (no RK)Rank-197.3—Unverified
4SOLIDER +UFFM+AMCRank-197—Unverified
5Unsupervised Pre-training (ResNet101+MGN)Rank-197—Unverified
6RGT&RGPR (RK)Rank-196.9—Unverified
7SOLIDERRank-196.9—Unverified
8LightMBN (RR)Rank-196.8—Unverified
9Viewpoint-Aware Loss(RK)Rank-196.79—Unverified
10TransReID-SSL (ViT-B w/o RK)Rank-196.7—Unverified
#ModelMetricClaimedVerifiedStatus
1DenseILmAP97.1—Unverified
2CTL Model (ResNet50, 256x128)mAP96.1—Unverified
3BPBreID (RK)mAP92.9—Unverified
4Unsupervised Pre-training (ResNet101+RK)mAP92.77—Unverified
5RGT&RGPR (RK)mAP92.7—Unverified
6st-ReID(RE, RK,Cam)mAP92.7—Unverified
7Viewpoint-Aware Loss(RK)mAP91.8—Unverified
8LDS (ResNet50 + RK)mAP91—Unverified
9Adaptive L2 Regularization (with re-ranking)mAP90.7—Unverified
10FlipReID (with re-ranking)mAP90.7—Unverified