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 801–850 of 1488 papers

TitleStatusHype
Learning Domain Invariant Representations for Generalizable Person Re-Identification—0
Leaning Compact and Representative Features for Cross-Modality Person Re-IdentificationCode0
Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification—0
Reference-Aided Part-Aligned Feature Disentangling for Video Person Re-Identification—0
Unsupervised and self-adaptative techniques for cross-domain person re-identificationCode0
Dense Interaction Learning for Video-based Person Re-identification—0
Bridging the Distribution Gap of Visible-Infrared Person Re-identification with Modality Batch Normalization—0
Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering—0
Domain Adaptive Egocentric Person Re-identification—0
Unified Batch All Triplet Loss for Visible-Infrared Person Re-identification—0
Domain Generalization: A Survey—0
SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification—0
Person Re-identification based on Robust Features in Open-world—0
CUPR: Contrastive Unsupervised Learning for Person Re-identification—0
Deep Miner: A Deep and Multi-branch Network which Mines Rich and Diverse Features for Person Re-identificationCode0
Multi-Attribute Enhancement Network for Person SearchCode0
Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification—0
AXM-Net: Implicit Cross-Modal Feature Alignment for Person Re-identification—0
Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains—0
Unsupervised Noisy Tracklet Person Re-identification—0
Take More Positives: An Empirical Study of Contrastive Learing in Unsupervised Person Re-Identification—0
Resolution-invariant Person ReID Based on Feature Transformation and Self-weighted Attention—0
HAVANA: Hierarchical and Variation-Normalized Autoencoder for Person Re-identification—0
Learn by Guessing: Multi-Step Pseudo-Label Refinement for Person Re-Identification—0
Clothing Status Awareness for Long-Term Person Re-Identification—0
Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation—0
Syncretic Modality Collaborative Learning for Visible Infrared Person Re-Identification—0
Occlude Them All: Occlusion-Aware Attention Network for Occluded Person Re-ID—0
Occluded Person Re-Identification With Single-Scale Global Representations—0
Kernel Methods in Hyperbolic Spaces—0
Attack-Guided Perceptual Data Generation for Real-World Re-Identification—0
Meta Pairwise Relationship Distillation for Unsupervised Person Re-IdentificationCode0
Learning To Know Where To See: A Visibility-Aware Approach for Occluded Person Re-Identification—0
BV-Person: A Large-Scale Dataset for Bird-View Person Re-Identification—0
Weakly Supervised Text-Based Person Re-IdentificationCode0
Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-Identification—0
Person Re-identification with Adversarial Triplet Embedding—0
Adversarial Multi-scale Feature Learning for Person Re-identification—0
ANL: Anti-Noise Learning for Cross-Domain Person Re-Identification—0
Person Re-Identification using Deep Learning Networks: A Systematic Review—0
Vehicle Re-identification Based on Dual Distance Center Loss—0
Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification—0
One for More: Selecting Generalizable Samples for Generalizable ReID Model—0
Multi-Domain Adversarial Feature Generalization for Person Re-Identification—0
Imbalance Robust Softmax for Deep Embeeding Learning—0
Learning Local Feature Descriptors for Multiple Object Tracking—0
PoseTrackReID: Dataset Description—0
DSAM: A Distance Shrinking with Angular Marginalizing Loss for High Performance Vehicle Re-identificatio—0
Domain Adaptive Person Re-Identification via Coupling OptimizationCode0
Unsupervised Attention Based Instance Discriminative Learning 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
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