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 1376–1400 of 1488 papers

TitleStatusHype
Learning Invariant Color Features for Person Re-Identification—0
Learning landmark guided embeddings for animal re-identification—0
Learning Local Feature Descriptors for Multiple Object Tracking—0
Learning Longterm Representations for Person Re-Identification Using Radio Signals—0
Learning Memory-Augmented Unidirectional Metrics for Cross-Modality Person Re-Identification—0
Learning Mid-level Filters for Person Re-identification—0
Learning Person Re-identification Models from Videos with Weak Supervision—0
Learning Posterior and Prior for Uncertainty Modeling in Person Re-Identification—0
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification—0
Learning Resolution-Invariant Deep Representations for Person Re-Identification—0
Learning Shape Representations for Clothing Variations in Person Re-Identification—0
Visual Similarity Attention—0
Learning to Adapt Invariance in Memory for Person Re-identification—0
Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification—0
Learning to Balance: Diverse Normalization for Cloth-Changing Person Re-Identification—0
Learning To Know Where To See: A Visibility-Aware Approach for Occluded Person Re-Identification—0
Learning to Learn in a Semi-Supervised Fashion—0
Learning to Learn Transferable Generative Attack for Person Re-Identification—0
Learning to rank in person re-identification with metric ensembles—0
Learning to Reduce Dual-Level Discrepancy for Infrared-Visible Person Re-Identification—0
Learning Towards the Largest Margins—0
Learning View-Specific Deep Networks for Person Re-Identification—0
Let Features Decide for Themselves: Feature Mask Network for Person Re-identification—0
Lifelong Person Re-Identification with Backward-Compatibility—0
Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation—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