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
Event-Guided Person Re-Identification via Sparse-Dense Complementary Learning—0
Evolution of ReID: From Early Methods to LLM Integration—0
Exciting-Inhibition Network for Person Reidentification in Internet of Things—0
Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification—0
Exploiting Transitivity for Learning Person Re-Identification Models on a Budget—0
Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning—0
Exploring Invariant Representation for Visible-Infrared Person Re-Identification—0
Exploring Modality-shared Appearance Features and Modality-invariant Relation Features for Cross-modality Person Re-Identification—0
Exploring Part-Informed Visual-Language Learning for Person Re-Identification—0
Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences—0
Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identification—0
Exploring the Camera Bias of Person Re-identification—0
Exploring the Quality of GAN Generated Images for Person Re-Identification—0
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems—0
Extended Cross-Modality United Learning for Unsupervised Visible-Infrared Person Re-identification—0
Factorized Distillation: Training Holistic Person Re-identification Model by Distilling an Ensemble of Partial ReID Models—0
Fairest of Them All: Establishing a Strong Baseline for Cross-Domain Person ReID—0
FAM: Visual Explanations for the Feature Representations From Deep Convolutional Networks—0
Fast Person Re-Identification via Cross-Camera Semantic Binary Transformation—0
FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks—0
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification—0
Feature Completion Transformer for Occluded Person Re-identification—0
Feature Disentanglement Learning with Switching and Aggregation for Video-based Person Re-Identification—0
Feature-Distribution Perturbation and Calibration for Generalized Person ReID—0
Feature Diversity Learning with Sample Dropout for Unsupervised Domain Adaptive Person Re-identification—0
Features for Multi-Target Multi-Camera Tracking and Re-Identification—0
Features Reconstruction Disentanglement Cloth-Changing Person Re-Identification—0
Federated and Generalized Person Re-identification through Domain and Feature Hallucinating—0
Few-Shot Deep Adversarial Learning for Video-based Person Re-identification—0
Fine-Grained Shape-Appearance Mutual Learning for Cloth-Changing Person Re-Identification—0
A Flow-Guided Mutual Attention Network for Video-Based Person Re-Identification—0
FMCNet: Feature-Level Modality Compensation for Visible-Infrared Person Re-Identification—0
FMT:Fusing Multi-task Convolutional Neural Network for Person Search—0
Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification—0
Frequency Domain Modality-invariant Feature Learning for Visible-infrared Person Re-Identification—0
Frequency Domain Nuances Mining for Visible-Infrared Person Re-identification—0
Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification—0
Pose-driven Attention-guided Image Generation for Person Re-Identification—0
Pose-driven Deep Convolutional Model for Person Re-identification—0
Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification—0
Pose Invariant Embedding for Deep Person Re-identification—0
PoseTrackReID: Dataset Description—0
Pose Transferrable Person Re-Identification—0
Pose-Transformation and Radial Distance Clustering for Unsupervised Person Re-identification—0
Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification—0
PRISM: Person Re-Identification via Structured Matching—0
Progressive Cross-camera Soft-label Learning for Semi-supervised Person Re-identification—0
Progressive Learning Algorithm for Efficient Person Re-Identification—0
Progressive Local Filter Pruning for Image Retrieval Acceleration—0
Prompt Decoupling for Text-to-Image 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
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