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 1351–1400 of 1488 papers

TitleStatusHype
Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains—0
Label Distribution Learning for Generalizable Multi-source Person Re-identification—0
Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification—0
Large-Scale Unsupervised Person Re-Identification with Contrastive Learning—0
LATex: Leveraging Attribute-based Text Knowledge for Aerial-Ground Person Re-Identification—0
Learn by Guessing: Multi-Step Pseudo-Label Refinement for Person Re-Identification—0
Learning 3D Shape Feature for Texture-Insensitive Person Re-Identification—0
Learning adaptively from the unknown for few-example video person re-ID—0
Learning a Discriminative Null Space for Person Re-identification—0
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification—0
Learning Compact Appearance Representation for Video-based Person Re-Identification—0
Learning Comprehensive Representations with Richer Self for Text-to-Image Person Re-Identification—0
Learning Concordant Attention via Target-aware Alignment for Visible-Infrared Person Re-identification—0
Learning Context-Aware Embedding for Person Search—0
Learning Context Graph for Person Search—0
Learning Correspondence Structures for Person Re-identification—0
Learning Cross-modality Information Bottleneck Representation for Heterogeneous Person Re-Identification—0
Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification—0
Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals—0
Learning Deep Representations by Mutual Information for Person Re-identification—0
Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification—0
Learning Domain Invariant Representations for Generalizable Person Re-Identification—0
Learning Efficient Image Representation for Person Re-Identification—0
Learning Feature Aggregation in Temporal Domain for Re-Identification—0
Learning Incremental Triplet Margin for Person Re-identification—0
Learning Invariant Color Features for Person Re-Identification—0
Learning landmark guided embeddings for animal re-identification—0
Learning Local Feature Descriptors for Multiple Object Tracking—0
Learning Longterm Representations for Person Re-Identification Using Radio Signals—0
Learning Memory-Augmented Unidirectional Metrics for Cross-Modality Person Re-Identification—0
Learning Mid-level Filters for Person Re-identification—0
Learning Person Re-identification Models from Videos with Weak Supervision—0
Learning Posterior and Prior for Uncertainty Modeling in Person Re-Identification—0
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification—0
Learning Resolution-Invariant Deep Representations for Person Re-Identification—0
Learning Shape Representations for Clothing Variations in Person Re-Identification—0
Visual Similarity Attention—0
Learning to Adapt Invariance in Memory for Person Re-identification—0
Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification—0
Learning to Balance: Diverse Normalization for Cloth-Changing Person Re-Identification—0
Learning To Know Where To See: A Visibility-Aware Approach for Occluded Person Re-Identification—0
Learning to Learn in a Semi-Supervised Fashion—0
Learning to Learn Transferable Generative Attack for Person Re-Identification—0
Learning to rank in person re-identification with metric ensembles—0
Learning to Reduce Dual-Level Discrepancy for Infrared-Visible Person Re-Identification—0
Learning Towards the Largest Margins—0
Learning View-Specific Deep Networks for Person Re-Identification—0
Let Features Decide for Themselves: Feature Mask Network for Person Re-identification—0
Lifelong Person Re-Identification with Backward-Compatibility—0
Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation—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