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

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Showing 1076–1100 of 1488 papers

TitleStatusHype
Cross-Modality Transformer for Visible-Infrared Person Re-Identification—0
Cross-modal Local Shortest Path and Global Enhancement for Visible-Thermal Person Re-Identification—0
Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond—0
Cross-Resolution Person Re-identification with Deep Antithetical Learning—0
Cross-Spectral Attention for Unsupervised RGB-IR Face Verification and Person Re-identification—0
Cross-Spectral Body Recognition with Side Information Embedding: Benchmarks on LLCM and Analyzing Range-Induced Occlusions on IJB-MDF—0
Cross-Spectrum Dual-Subspace Pairing for RGB-infrared Cross-Modality Person Re-Identification—0
Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars—0
CUPR: Contrastive Unsupervised Learning for Person Re-identification—0
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions—0
CycleTrans: Learning Neutral yet Discriminative Features for Visible-Infrared Person Re-Identification—0
DaliID: Distortion-Adaptive Learned Invariance for Identification Models—0
DARI: Distance metric And Representation Integration for Person Verification—0
DART^3: Leveraging Distance for Test Time Adaptation in Person Re-Identification—0
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs—0
DDRN:a Data Distribution Reconstruction Network for Occluded Person Re-Identification—0
Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification—0
Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification—0
Decentralised Person Re-Identification with Selective Knowledge Aggregation—0
Deep Active Learning for Video-based Person Re-identification—0
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification—0
Deep Attributes Driven Multi-Camera Person Re-identification—0
Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification—0
Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification—0
Deep Feature Learning with Relative Distance Comparison for Person Re-identification—0
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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
10SOLIDER (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
5st-ReID(RE, RK,Cam)mAP92.7—Unverified
6RGT&RGPR (RK)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