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 701–750 of 1488 papers

TitleStatusHype
Stronger Baseline for Person Re-Identification—0
Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-IdentificationCode0
Subtask-dominated Transfer Learning for Long-tail Person Search—0
Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive FrameworkCode0
MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark—0
PGGANet: Pose Guided Graph Attention Network for Person Re-identification—0
TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification—0
Learning Context-Aware Embedding for Person Search—0
Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation—0
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification—0
Improving Person Re-Identification with Temporal Constraints—0
Learning to Disentangle Scenes for Person Re-identificationCode0
Exploiting Robust Unsupervised Video Person Re-identificationCode0
Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action RecognitionCode0
Unsupervised Person Re-Identification with Wireless Positioning under Weak Scene LabelingCode0
MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification—0
DEX: Domain Embedding Expansion for Generalized Person Re-identification—0
Decentralised Person Re-Identification with Selective Knowledge Aggregation—0
CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification—0
Exciting-Inhibition Network for Person Reidentification in Internet of Things—0
Rectifying the Data Bias in Knowledge Distillation—0
Deep learning-based person re-identification methods: A survey and outlook of recent works—0
MilliTRACE-IR: Contact Tracing and Temperature Screening via mm-Wave and Infrared Sensing—0
Context-Aware Unsupervised Clustering for Person Search—0
Video Temporal Relationship Mining for Data-Efficient Person Re-identification—0
A Technical Report for ICCV 2021 VIPriors Re-identification Challenge—0
Benchmarking person re-identification approaches and training datasets for practical real-world implementations—0
Generalizable Person Re-identification Without Demographics—0
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id—0
Camera Bias Regularization for Person Re-identification—0
OH-Former: Omni-Relational High-Order Transformer for Person Re-Identification—0
Resolution based Feature Distillation for Cross Resolution Person Re-Identification—0
Global-Local Dynamic Feature Alignment Network for Person Re-Identification—0
Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification—0
Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions—0
Making Person Search Enjoy the Merits of Person Re-identification—0
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency—0
Exploring the Quality of GAN Generated Images for Person Re-Identification—0
The Multi-Modal Video Reasoning and Analyzing Competition—0
On the Importance of Encrypting Deep FeaturesCode0
Video Person Re-identification using Attribute-enhanced Features—0
Unsupervised Person Re-identification with Stochastic Training StrategyCode0
Multi-granularity for knowledge distillationCode0
Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification—0
Multigranular Visual-Semantic Embedding for Cloth-Changing Person Re-identification—0
Towards Discriminative Representation Learning for Unsupervised Person Re-identification—0
Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification—0
Attribute Guided Sparse Tensor-Based Model for Person Re-Identification—0
Spatio-Temporal Representation Factorization for Video-based Person Re-Identification—0
Going Deeper into Semi-supervised 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