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 1151–1200 of 1488 papers

TitleStatusHype
Occlude Them All: Occlusion-Aware Attention Network for Occluded Person Re-ID—0
Occlusion-Robust Online Multi-Object Visual Tracking using a GM-PHD Filter with CNN-Based Re-Identification—0
OH-Former: Omni-Relational High-Order Transformer for Person Re-Identification—0
Omni-directional Feature Learning for Person Re-identification—0
One for More: Selecting Generalizable Samples for Generalizable ReID Model—0
One-pass Person Re-identification by Sketch Online Discriminant Analysis—0
One-Shot Metric Learning for Person Re-Identification—0
Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting—0
Online Joint Multi-Metric Adaptation From Frequent Sharing-Subset Mining for Person Re-Identification—0
Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-Identification—0
Online Unsupervised Domain Adaptation for Person Re-identification—0
On the Stability and Generalization of Triplet Learning—0
OpenAnimals: Revisiting Person Re-Identification for Animals Towards Better Generalization—0
Open-set Person Re-identification—0
Open-World Pose Transfer via Sequential Test-Time Adaption—0
Operator-in-the-Loop Deep Sequential Multi-camera Feature Fusion for Person Re-identification—0
OptiGait-LGBM: An Efficient Approach of Gait-based Person Re-identification in Non-Overlapping Regions—0
Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation—0
Ordered or Orderless: A Revisit for Video based Person Re-Identification—0
Push for Center Learning via Orthogonalization and Subspace Masking for Person Re-Identification—0
Outlier Cluster Formation in Spectral Clustering—0
Overlap Suppression Clustering for Offline Multi-Camera People Tracking—0
PAC-GAN: An Effective Pose Augmentation Scheme for Unsupervised Cross-View Person Re-identification—0
PAFormer: Part Aware Transformer for Person Re-identification—0
PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification—0
Parameter Hierarchical Optimization for Visible-Infrared Person Re-Identification—0
Part-Aligned Bilinear Representations for Person Re-identification—0
Part-Attention Based Model Make Occluded Person Re-Identification Stronger—0
Part-based Deep Hashing for Large-scale Person Re-identification—0
PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification—0
Partial Person Re-Identification—0
Partial Person Re-identification with Alignment and Hallucination—0
Partial Person Re-Identification With Part-Part Correspondence Learning—0
PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification—0
Pedestrian re-identification based on Tree branch network with local and global learning—0
Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition—0
Person30K: A Dual-Meta Generalization Network for Person Re-Identification—0
Person De-reidentification: A Variation-guided Identity Shift Modeling—0
Person image generation with semantic attention network for person re-identification—0
PersonMAE: Person Re-Identification Pre-Training with Masked AutoEncoders—0
Person re-identification across different datasets with multi-task learning—0
Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends—0
Person Re-identification Based on Color Histogram and Spatial Configuration of Dominant Color Regions—0
Improved Res2Net model for Person re-identification—0
Person Re-identification based on Robust Features in Open-world—0
Person Re-identification by analyzing Dynamic Variations in Gait Sequences—0
Person Re-Identification by Camera Correlation Aware Feature Augmentation—0
Person Re-Identification by Context-aware Part Attention and Multi-Head Collaborative Learning—0
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification—0
Person Re-Identification by Discriminative Selection in Video Ranking—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