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 751–775 of 1488 papers

TitleStatusHype
GiT: Graph Interactive Transformer for Vehicle Re-identification—0
Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification—0
Domain adaptation for person re-identification on new unlabeled data using AlignedReID++Code0
Domain-Class Correlation Decomposition for Generalizable Person Re-Identification—0
Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-IdentificationCode0
Feature Completion for Occluded Person Re-IdentificationCode0
The Arm-Swing Is Discriminative in Video Gait Recognition for Athlete Re-Identification—0
Learning 3D Shape Feature for Texture-Insensitive Person Re-Identification—0
Fine-Grained Shape-Appearance Mutual Learning for Cloth-Changing Person Re-Identification—0
Person Re-Identification Using Heterogeneous Local Graph Attention Networks—0
Partial Person Re-Identification With Part-Part Correspondence Learning—0
Person30K: A Dual-Meta Generalization Network for Person Re-Identification—0
Coarse-To-Fine Person Re-Identification With Auxiliary-Domain Classification and Second-Order Information Bottleneck—0
G2DA: Geometry-Guided Dual-Alignment Learning for RGB-Infrared Person Re-Identification—0
Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-IdentificationCode0
Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification—0
Unsupervised Video Person Re-identification via Noise and Hard frame Aware Clustering—0
Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer—0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification—0
Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification—0
Improved Instance Discrimination and Feature Compactness for End-to-End Person SearchCode0
Generalizable Person Re-identification with Relevance-aware Mixture of Experts—0
Large-Scale Unsupervised Person Re-Identification with Contrastive Learning—0
Neighbourhood-guided Feature Reconstruction for Occluded 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
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