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 1201–1225 of 1488 papers

TitleStatusHype
Multi-shot Person Re-identification through Set Distance with Visual Distributional Representation—0
Deep Group-shuffling Random Walk for Person Re-identificationCode0
End-to-End Deep Kronecker-Product Matching for Person Re-identificationCode0
Hard-Aware Point-to-Set Deep Metric for Person Re-identificationCode0
Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identificationCode0
Maximum Margin Metric Learning Over Discriminative Nullspace for Person Re-identification—0
Adversarial Open-World Person Re-Identification—0
Person Search in Videos with One Portrait Through Visual and Temporal LinksCode0
Person Re-identification with Deep Similarity-Guided Graph Neural Network—0
Person re-identification across different datasets with multi-task learning—0
Partial Person Re-identification with Alignment and Hallucination—0
Improving Deep Models of Person Re-identification for Cross-Dataset Usage—0
Person Search via A Mask-Guided Two-Stream CNN Model—0
Operator-in-the-Loop Deep Sequential Multi-camera Feature Fusion for Person Re-identification—0
Metric Embedding Autoencoders for Unsupervised Cross-Dataset Transfer Learning—0
Spatial-Temporal Synergic Residual Learning for Video Person Re-Identification—0
SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification—0
Improved Person Re-Identification Based on Saliency and Semantic Parsing with Deep Neural Network ModelsCode0
Survey on Deep Learning Techniques for Person Re-Identification Task—0
Video-based Person Re-identification via 3D Convolutional Networks and Non-local Attention—0
Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification—0
Discriminative Feature Learning with Foreground Attention for Person Re-Identification—0
A Spatial and Temporal Features Mixture Model with Body Parts for Video-based Person Re-Identification—0
SphereReID: Deep Hypersphere Manifold Embedding for Person Re-IdentificationCode0
Person Re-Identification in Identity Regression Space—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