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 626–650 of 1488 papers

TitleStatusHype
Collaborative Attention Network for Person Re-identification—0
Attention Deep Model with Multi-Scale Deep Supervision for Person Re-Identification—0
Enhancing Long-Term Person Re-Identification Using Global, Local Body Part, and Head Streams—0
Enhanced Visual-Semantic Interaction with Tailored Prompts for Pedestrian Attribute Recognition—0
COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification—0
Energy Clustering for Unsupervised Person Re-identification—0
End-to-End Training of CNN Ensembles for Person Re-Identification—0
Coarse-To-Fine Person Re-Identification With Auxiliary-Domain Classification and Second-Order Information Bottleneck—0
End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification—0
Coarse Attribute Prediction with Task Agnostic Distillation for Real World Clothes Changing ReID—0
Attention-based Shape and Gait Representations Learning for Video-based Cloth-Changing Person Re-Identification—0
Attention-based Few-Shot Person Re-identification Using Meta Learning—0
End-to-End Context-Aided Unicity Matching for Person Re-identification—0
End-to-End Comparative Attention Networks for Person Re-identification—0
CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification—0
Embedding Deep Metric for Person Re-identication A Study Against Large Variations—0
Embedding and Enriching Explicit Semantics for Visible-Infrared Person Re-Identification—0
Attention-Aware Compositional Network for Person Re-identification—0
Eliminating Background-Bias for Robust Person Re-Identification—0
EgoReID Dataset: Person Re-identification in Videos Acquired by Mobile Devices with First-Person Point-of-View—0
EgoReID: Cross-view Self-Identification and Human Re-identification in Egocentric and Surveillance Videos—0
Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification—0
Cluster Loss for Person Re-Identification—0
Attention: A Big Surprise for Cross-Domain Person Re-Identification—0
Efficient Online Local Metric Adaptation via Negative Samples 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
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