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 626–650 of 1488 papers

TitleStatusHype
Dynamic Template Initialization for Part-Aware Person Re-ID—0
CycleTrans: Learning Neutral yet Discriminative Features for Visible-Infrared Person Re-Identification—0
Synthetic Data in Human Analysis: A Survey—0
Domain Camera Adaptation and Collaborative Multiple Feature Clustering for Unsupervised Person Re-ID—0
Counterfactual Intervention Feature Transfer for Visible-Infrared Person Re-identification—0
Deep Learning-based Occluded Person Re-identification: A Survey—0
Look Closer to Your Enemy: Learning to Attack via Teacher-Student MimickingCode0
Portrait Interpretation and a Benchmark—0
A Semantic-aware Attention and Visual Shielding Network for Cloth-changing Person Re-identification—0
Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars—0
Towards Privacy-Preserving Person Re-identification via Person Identify Shift—0
Rapid Person Re-Identification via Sub-space Consistency Regularization—0
Dynamic Gradient Reactivation for Backward Compatible Person Re-identification—0
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification—0
Pseudo-Pair based Self-Similarity Learning for Unsupervised Person Re-identification—0
Style Interleaved Learning for Generalizable Person Re-identificationCode0
Context Sensing Attention Network for Video-based Person Re-identification—0
Unsupervised Learning for Human Sensing Using Radio Signals—0
Adversarial Pairwise Reverse Attention for Camera Performance Imbalance in Person Re-identification: New Dataset and Metrics—0
Learning Towards the Largest Margins—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Towards Generalizable Person Re-identification with a Bi-stream Generative Model—0
Plug-and-Play Pseudo Label Correction Network for Unsupervised Person Re-identification—0
Cross-modal Local Shortest Path and Global Enhancement for Visible-Thermal Person Re-Identification—0
Feature-Distribution Perturbation and Calibration for Generalized Person ReID—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