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 1326–1350 of 1488 papers

TitleStatusHype
A Two Stream Siamese Convolutional Neural Network for Person Re-Identification—0
Efficient Online Local Metric Adaptation via Negative Samples for Person Re-Identification—0
RGB-Infrared Cross-Modality Person Re-Identification—0
Stepwise Metric Promotion for Unsupervised Video Person Re-Identification—0
HydraPlus-Net: Attentive Deep Features for Pedestrian AnalysisCode0
Dynamic Label Graph Matching for Unsupervised Video Re-Identification—0
Pose-driven Deep Convolutional Model for Person Re-identification—0
Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition—0
Multi-scale Deep Learning Architectures for Person Re-identification—0
GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval—0
Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identificationCode0
Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification—0
Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals—0
Divide and Fuse: A Re-ranking Approach for Person Re-identification—0
Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-IdentificationCode0
Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding—0
Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification—0
Person Re-identification Using Visual Attention—0
Deeply-Learned Part-Aligned Representations for Person Re-IdentificationCode0
What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification—0
Deep Reinforcement Learning Attention Selection for Person Re-Identification—0
Learning Efficient Image Representation for Person Re-Identification—0
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile DevicesCode0
Deep Representation Learning with Part Loss for Person Re-Identification—0
Deep Ranking Model by Large Adaptive Margin Learning for 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
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