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 451–500 of 1488 papers

TitleStatusHype
MagnifierNet: Towards Semantic Adversary and Fusion for Person Re-identificationCode0
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled DataCode0
Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-IdentificationCode0
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free ApproachCode0
Multi-view Information Integration and Propagation for Occluded Person Re-identificationCode0
Leveraging Virtual and Real Person for Unsupervised Person Re-identificationCode0
Learning Transferable Pedestrian Representation from Multimodal Information SupervisionCode0
Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identificationCode0
Learning to Disentangle Scenes for Person Re-identificationCode0
Learning from Synchronization: Self-Supervised Uncalibrated Multi-View Person Association in Challenging ScenesCode0
Learning Intra and Inter-Camera Invariance for Isolated Camera Supervised Person Re-identificationCode0
Resolution-invariant Person Re-IdentificationCode0
Deep Person Re-Identification with Improved Embedding and Efficient TrainingCode0
Learning Disentangled Representation for Robust Person Re-identificationCode0
Deep-Person: Learning Discriminative Deep Features for Person Re-IdentificationCode0
Deep Neural Networks with Inexact Matching for Person Re-IdentificationCode0
Learning Discriminative Features with Multiple Granularities for Person Re-IdentificationCode0
Learning Invariance from Generated Variance for Unsupervised Person Re-identificationCode0
3C: Confidence-Guided Clustering and Contrastive Learning for Unsupervised Person Re-IdentificationCode0
Enhancing Person Re-identification in a Self-trained SubspaceCode0
Deep Mutual LearningCode0
Deep Multimodal Fusion for Generalizable Person Re-identificationCode0
Deep Miner: A Deep and Multi-branch Network which Mines Rich and Diverse Features for Person Re-identificationCode0
Leaning Compact and Representative Features for Cross-Modality Person Re-IdentificationCode0
BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-IdentificationCode0
Deep Meta Metric LearningCode0
Deeply-Learned Part-Aligned Representations for Person Re-IdentificationCode0
A Discriminatively Learned CNN Embedding for Person Re-identificationCode0
LCM: Log Conformal Maps for Robust Representation Learning to Mitigate Perspective DistortionCode0
Deeply-Coupled Convolution-Transformer with Spatial-temporal Complementary Learning for Video-based Person Re-identificationCode0
Beyond triplet loss: a deep quadruplet network for person re-identificationCode0
Joint Progressive Knowledge Distillation and Unsupervised Domain AdaptationCode0
An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-IdentificationCode0
Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-IdentificationCode0
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)Code0
Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-IdentificationCode0
Beyond Intra-modality: A Survey of Heterogeneous Person Re-identificationCode0
Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identificationCode0
Beyond Human Parts: Dual Part-Aligned Representations for Person Re-IdentificationCode0
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identificationCode0
Deep Group-shuffling Random Walk for Person Re-identificationCode0
Deep Fusion Feature Representation Learning with Hard Mining Center-Triplet Loss for Person Re-identificationCode0
Invisible Backdoor Attack with Dynamic Triggers against Person Re-identificationCode0
In Defense of the Triplet Loss for Person Re-IdentificationCode0
Beyond Domain Gap: Exploiting Subjectivity in Sketch-Based Person RetrievalCode0
Spatial and Temporal Mutual Promotion for Video-based Person Re-identificationCode0
In Defense of the Classification Loss for Person Re-IdentificationCode0
Spatial-Temporal Person Re-identificationCode0
In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label DistillationCode0
Interaction-and-Aggregation Network for Person Re-identificationCode0
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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