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 551–600 of 1488 papers

TitleStatusHype
Benchmarks for Corruption Invariant Person Re-identificationCode1
Unsupervised Person Re-Identification with Wireless Positioning under Weak Scene LabelingCode0
DEX: Domain Embedding Expansion for Generalized Person Re-identification—0
MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared 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
Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification SystemsCode1
MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identificationCode1
Rectifying the Data Bias in Knowledge Distillation—0
Rethinking Person Re-Identification via Semantic-Based PretrainingCode1
Deep learning-based person re-identification methods: A survey and outlook of recent works—0
2nd Place Solution to Google Landmark Retrieval 2021Code1
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
Camera Bias Regularization for Person Re-identification—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
Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-IdentificationCode1
OH-Former: Omni-Relational High-Order Transformer for Person Re-Identification—0
Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-IdentificationCode1
Homogeneous and Heterogeneous Relational Graph for Visible-infrared Person Re-identificationCode1
Memory Regulation and Alignment toward Generalizer RGB-Infrared PersonCode1
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
Salience-Guided Iterative Asymmetric Mutual Hashing for Fast Person Re-identificationCode1
Pose-guided Inter- and Intra-part Relational Transformer for Occluded Person Re-IdentificationCode1
Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions—0
Making Person Search Enjoy the Merits of Person Re-identification—0
Exploring the Quality of GAN Generated Images for Person Re-Identification—0
Ranking Models in Unlabeled New EnvironmentsCode1
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency—0
Video-based Person Re-identification with Spatial and Temporal Memory NetworksCode1
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identificationCode1
The Multi-Modal Video Reasoning and Analyzing Competition—0
Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal CorrespondencesCode1
Unsupervised Person Re-identification with Stochastic Training StrategyCode0
On the Importance of Encrypting Deep FeaturesCode0
Video Person Re-identification using Attribute-enhanced Features—0
Multi-granularity for knowledge distillationCode0
Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification—0
Semi-Supervised Domain Generalizable Person Re-IdentificationCode2
Person Re-identification via Attention PyramidCode1
Multigranular Visual-Semantic Embedding for Cloth-Changing Person Re-identification—0
Towards Discriminative Representation Learning for Unsupervised Person Re-identification—0
Unifying Nonlocal Blocks for Neural NetworksCode1
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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