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 851–875 of 1488 papers

TitleStatusHype
Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification—0
Unsupervised Domain-Adaptive Person Re-identification Based on Attributes—0
Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation—0
Unsupervised domain-adaptive person re-identification with multi-camera constraints—0
Unsupervised Domain Adaptive Person Re-id with Local-enhance and Prototype Dictionary Learning—0
Unsupervised Few Shot Learning via Self-supervised Training—0
Unsupervised Few-shot Learning via Self-supervised Training—0
Unsupervised Learning for Human Sensing Using Radio Signals—0
Unsupervised Long-Term Person Re-Identification with Clothes Change—0
Unsupervised multi-source domain adaptation for person re-identification via feature fusion and pseudo-label refinement—0
Unsupervised Noisy Tracklet Person Re-identification—0
Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions—0
Unsupervised Person Re-Identification by Camera-Aware Similarity Consistency Learning—0
Unsupervised Person Re-identification by Deep Learning Tracklet Association—0
Unsupervised Person Re-identification via Multi-label Classification—0
Unsupervised Person Re-identification via Simultaneous Clustering and Consistency Learning—0
Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering—0
Unsupervised Salience Learning for Person Re-identification—0
Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos—0
Unsupervised Video Person Re-identification via Noise and Hard frame Aware Clustering—0
Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement—0
Unsupervised Visible-Infrared ReID via Pseudo-label Correction and Modality-level Alignment—0
Unveiling personnel movement in a larger indoor area with a non-overlapping multi-camera system—0
User-Level Membership Inference Attack against Metric Embedding Learning—0
Using Auxiliary Information for Person Re-Identification -- A Tutorial Overview—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