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 1251–1300 of 1488 papers

TitleStatusHype
Generalizable Person Re-identification Without Demographics—0
Generalizable Person Re-Identification by Domain-Invariant Mapping Network—0
Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption—0
Generalizable Person Re-Identification via Viewpoint Alignment and Fusion—0
Generalizable Person Re-identification with Relevance-aware Mixture of Experts—0
Generalizable Re-Identification from Videos with Cycle Association—0
Generic Instance Search and Re-identification from One Example via Attributes and Categories—0
GetNet: Get Target Area for Image Pairing—0
GiT: Graph Interactive Transformer for Vehicle Re-identification—0
GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval—0
Global Distance-distributions Separation for Unsupervised Person Re-identification—0
Global-Local Dynamic Feature Alignment Network for Person Re-Identification—0
Global-Local Temporal Representations For Video Person Re-Identification—0
Going Deeper into Semi-supervised Person Re-identification—0
Grafted network for person re-identification—0
Graph Correspondence Transfer for Person Re-identification—0
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification—0
GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification—0
Group Consistent Similarity Learning via Deep CRF for Person Re-Identification—0
Grouped Adaptive Loss Weighting for Person Search—0
Group Membership Prediction—0
DotSCN: Group Re-identification via Domain-Transferred Single and Couple Representation Learning—0
Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding—0
Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification—0
HashReID: Dynamic Network with Binary Codes for Efficient Person Re-identification—0
HAVANA: Hierarchical and Variation-Normalized Autoencoder for Person Re-identification—0
HBReID: Harder Batch for Re-identification—0
Hetero-Center Loss for Cross-Modality Person Re-Identification—0
Heuristics2Annotate: Efficient Annotation of Large-Scale Marathon Dataset For Bounding Box Regression—0
Hierarchical and Efficient Learning for Person Re-Identification—0
Hierarchical Bi-Directional Feature Perception Network for Person Re-Identification—0
Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification—0
Hierarchical Cross Network for Person Re-identification—0
Hierarchical Gaussian Descriptor for Person Re-Identification—0
Hierarchical Gaussian Descriptors with Application to Person Re-Identification—0
Hierarchical Invariant Feature Learning with Marginalization for Person Re-Identification—0
Highly Efficient Regression for Scalable Person Re-Identification—0
Homocentric Hypersphere Feature Embedding for Person Re-identification—0
HorNet: A Hierarchical Offshoot Recurrent Network for Improving Person Re-ID via Image Captioning—0
How Image Generation Helps Visible-to-Infrared Person Re-Identification?—0
How important are faces for person re-identification?—0
HPILN: A feature learning framework for cross-modality person re-identification—0
Human-centered Interactive Learning via MLLMs for Text-to-Image Person Re-identification—0
Human-In-The-Loop Person Re-Identification—0
Human Re-identification by Matching Compositional Template with Cluster Sampling—0
Human Re-ID Meets LVLMs: What can we expect?—0
Human Semantic Parsing for Person Re-identification—0
Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification—0
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id—0
Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds—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