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 926–950 of 1488 papers

TitleStatusHype
Adaptive Re-ranking of Deep Feature for Person Re-identification—0
Adaptive Transfer Network for Cross-Domain Person Re-Identification—0
AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification—0
A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection—0
A Deep Hierarchical Feature Sparse Framework for Occluded Person Re-Identification—0
A Deep Structure of Person Re-Identification using Multi-Level Gaussian Models—0
Advancing Person Re-Identification: Tensor-based Feature Fusion and Multilinear Subspace Learning—0
Adversarial Attribute-Image Person Re-identification—0
Adversarial Binary Coding for Efficient Person Re-identification—0
Adversarial Camera Alignment Network for Unsupervised Cross-camera Person Re-identification—0
Adversarially Occluded Samples for Person Re-Identification—0
Adversarial Multi-scale Feature Learning for Person Re-identification—0
Adversarial Open-World Person Re-Identification—0
Adversarial Pairwise Reverse Attention for Camera Performance Imbalance in Person Re-identification: New Dataset and Metrics—0
A framework with updateable joint images re-ranking for Person Re-identification—0
A Free Lunch to Person Re-identification: Learning from Automatically Generated Noisy Tracklets—0
A heterogeneous branch and multi-level classification network for person re-identification—0
Aligned Divergent Pathways for Omni-Domain Generalized Person Re-Identification—0
A Little Bit Attention Is All You Need for Person Re-Identification—0
A Multi-task Deep Network for Person Re-identification—0
An Attention-driven Two-stage Clustering Method for Unsupervised Person Re-Identification—0
An Efficient Framework for Visible-Infrared Cross Modality Person Re-Identification—0
An End-to-End Foreground-Aware Network for Person Re-Identification—0
An Enhanced Deep Feature Representation for Person Re-identification—0
An Evaluation of Deep CNN Baselines for Scene-Independent 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
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