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 101–150 of 1488 papers

TitleStatusHype
CLIP-DFGS: A Hard Sample Mining Method for CLIP in Generalizable Person Re-Identification—0
ViFi-ReID: A Two-Stream Vision-WiFi Multimodal Approach for Person Re-identification—0
CLIP-SCGI: Synthesized Caption-Guided Inversion for Person Re-Identification—0
Aligned Divergent Pathways for Omni-Domain Generalized Person Re-Identification—0
Diverse Deep Feature Ensemble Learning for Omni-Domain Generalized Person Re-identification—0
A Unified Deep Semantic Expansion Framework for Domain-Generalized Person Re-identification—0
DDRN:a Data Distribution Reconstruction Network for Occluded Person Re-Identification—0
Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting—0
Learning to Balance: Diverse Normalization for Cloth-Changing Person Re-Identification—0
Revisiting the Role of Texture in 3D Person Re-identification—0
OpenAnimals: Revisiting Person Re-Identification for Animals Towards Better Generalization—0
ProFD: Prompt-Guided Feature Disentangling for Occluded Person Re-IdentificationCode1
Domain Consistency Representation Learning for Lifelong Person Re-IdentificationCode1
CLIP-based Camera-Agnostic Feature Learning for Intra-camera Person Re-IdentificationCode0
Cross-video Identity Correlating for Person Re-identification Pre-trainingCode2
LCM: Log Conformal Maps for Robust Representation Learning to Mitigate Perspective DistortionCode0
Tran-GCN: A Transformer-Enhanced Graph Convolutional Network for Person Re-Identification in Monitoring Videos—0
Disentangled Representations for Short-Term and Long-Term Person Re-Identification—0
Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identificationCode1
Learning to Learn Transferable Generative Attack for Person Re-Identification—0
Multi-Camera Industrial Open-Set Person Re-Identification and Tracking—0
PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification—0
WRIM-Net: Wide-Ranging Information Mining Network for Visible-Infrared Person Re-Identification—0
3C: Confidence-Guided Clustering and Contrastive Learning for Unsupervised Person Re-IdentificationCode0
Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation ApproachCode1
PAFormer: Part Aware Transformer for Person Re-identification—0
PersonViT: Large-scale Self-supervised Vision Transformer for Person Re-IdentificationCode1
PixelFade: Privacy-preserving Person Re-identification with Noise-guided Progressive ReplacementCode0
Keypoint Promptable Re-IdentificationCode3
Open-Set Biometrics: Beyond Good Closed-Set ModelsCode1
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
Beyond Augmentation: Empowering Model Robustness under Extreme Capture Environments—0
Mutual Information Guided Optimal Transport for Unsupervised Visible-Infrared Person Re-identification—0
Features Reconstruction Disentanglement Cloth-Changing Person Re-Identification—0
Time-Frequency Analysis of Variable-Length WiFi CSI Signals for Person Re-Identification—0
MARS: Paying more attention to visual attributes for text-based person searchCode1
The Balanced-Pairwise-Affinities Feature TransformCode2
Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification—0
Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-identification—0
Unleashing the Potential of Tracklets for Unsupervised Video Person Re-Identification—0
Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning—0
Overlap Suppression Clustering for Offline Multi-Camera People Tracking—0
CLIP-Driven Cloth-Agnostic Feature Learning for Cloth-Changing Person Re-Identification—0
DenoiseRep: Denoising Model for Representation LearningCode1
Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-TrainingCode1
CORE-ReID: Comprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-IdentificationCode1
Distribution Aligned Semantics Adaption for Lifelong Person Re-IdentificationCode0
DiffPhysBA: Diffusion-based Physical Backdoor Attack against Person Re-Identification in Real-World—0
ENTIRe-ID: An Extensive and Diverse Dataset for Person Re-IdentificationCode1
Auto-selected Knowledge Adapters for Lifelong Person Re-identification—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