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 951–1000 of 1488 papers

TitleStatusHype
AANet: Attribute Attention Network for Person Re-Identifications—0
Simulating Content Consistent Vehicle Datasets with Attribute DescentCode1
In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label DistillationCode0
Progressive Learning Algorithm for Efficient Person Re-Identification—0
Occlusion-Robust Online Multi-Object Visual Tracking using a GM-PHD Filter with CNN-Based Re-Identification—0
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-IdentificationCode0
Viewpoint-Aware Loss with Angular Regularization for Person Re-IdentificationCode0
Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-IdentificationCode0
Collaborative Attention Network for Person Re-identification—0
Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time AspectCode1
Spatial-Aware GAN for Unsupervised Person Re-identification—0
GBCNs: Genetic Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs—0
AttKGCN: Attribute Knowledge Graph Convolutional Network for Person Re-identification—0
Attention Deep Model with Multi-Scale Deep Supervision for Person Re-Identification—0
Video Person Re-ID: Fantastic Techniques and Where to Find ThemCode1
Relation Network for Person Re-identificationCode0
ID-aware Quality for Set-based Person Re-identificationCode0
Unified Multifaceted Feature Learning for Person Re-Identification—0
Visual Similarity Attention—0
DeepPFCN: Deep Parallel Feature Consensus Network For Person Re-Identification—0
Distribution Context Aware Loss for Person Re-identification—0
Unsupervised Deep Metric Learning via Auxiliary Rotation Loss—0
A Spectral Nonlocal Block for Neural Networks—0
Progressive Sample Mining and Representation Learning for One-Shot Person Re-identification with Adversarial SamplesCode0
Weakly Supervised Tracklet Person Re-Identification by Deep Feature-wise Mutual Learning—0
Beyond Universal Person Re-ID Attack—0
Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification—0
Multi-Resolution Overlapping Stripes Network for Person Re-Identification—0
Learning Disentangled Representation for Robust Person Re-identificationCode0
Attend to the Difference: Cross-Modality Person Re-identification via Contrastive Correlation—0
An End-to-End Foreground-Aware Network for Person Re-Identification—0
Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal RegularizationCode0
Torchreid: A Library for Deep Learning Person Re-Identification in PytorchCode1
Beyond Human Parts: Dual Part-Aligned Representations for Person Re-IdentificationCode0
Hetero-Center Loss for Cross-Modality Person Re-Identification—0
Video Person Re-Identification using Learned Clip Similarity Aggregation—0
Compact Network Training for Person ReID—0
Learning Generalisable Omni-Scale Representations for Person Re-IdentificationCode1
RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature AlignmentCode0
CHD:Consecutive Horizontal Dropout for Human Gait Feature Extraction—0
Augmented Hard Example Mining for Generalizable Person Re-Identification—0
A Semi-Supervised Maximum Margin Metric Learning Approach for Small Scale Person Re-identification—0
Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification—0
View Confusion Feature Learning for Person Re-identification—0
GetNet: Get Target Area for Image Pairing—0
Improved Res2Net model for Person re-identification—0
Improving One-shot NAS by Suppressing the Posterior Fading—0
AlignedReID++: Dynamically matching local information for person re-identificationCode0
Discriminative Feature Learning With Consistent Attention Regularization for Person Re-Identification—0
Unsupervised Graph Association 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