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 601–650 of 1488 papers

TitleStatusHype
Deep Learning for Video-based Person Re-Identification: A Survey—0
Joint Person Re-identification and Camera Network Topology Inference in Multiple Cameras—0
Joint Probabilistic Matching Using m-Best Solutions—0
A Convolutional Baseline for Person Re-Identification Using Vision and Language Descriptions—0
Illumination-Adaptive Person Re-identification—0
Deep learning-based person re-identification methods: A survey and outlook of recent works—0
Deep Learning based Person Re-identification—0
Deep Learning-based Occluded Person Re-identification: A Survey—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification—0
Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification—0
Knowledge Assembly: Semi-Supervised Multi-Task Learning from Multiple Datasets with Disjoint Labels—0
Learning Compact Appearance Representation for Video-based Person Re-Identification—0
Deep Hybrid Similarity Learning for Person Re-identification—0
Identity-aware Feature Decoupling Learning for Clothing-change Person Re-identification—0
Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection—0
Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds—0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
Iterated Support Vector Machines for Distance Metric Learning—0
Joint Discriminative and Metric Embedding Learning for Person Re-Identification—0
A Deep Structure of Person Re-Identification using Multi-Level Gaussian Models—0
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id—0
Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification—0
Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification—0
iQIYI-VID: A Large Dataset for Multi-modal Person Identification—0
Human Semantic Parsing for Person Re-identification—0
Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification—0
Human Re-ID Meets LVLMs: What can we expect?—0
Deep Feature Learning with Relative Distance Comparison for Person Re-identification—0
ANL: Anti-Noise Learning for Cross-Domain Person Re-Identification—0
Human Re-identification by Matching Compositional Template with Cluster Sampling—0
Beyond Augmentation: Empowering Model Robustness under Extreme Capture Environments—0
Is Synthetic Dataset Reliable for Benchmarking Generalizable Person Re-Identification?—0
Identity-aware Dual-constraint Network for Cloth-Changing Person Re-identification—0
Human-In-The-Loop Person Re-Identification—0
Identity-Clothing Similarity Modeling for Unsupervised Clothing Change Person Re-Identification—0
An Introduction to Person Re-identification with Generative Adversarial Networks—0
Identity Preserving Generative Adversarial Network for Cross-Domain Person Re-identification—0
Human-centered Interactive Learning via MLLMs for Text-to-Image Person Re-identification—0
HPILN: A feature learning framework for cross-modality person re-identification—0
Id-Free Person Similarity Learning—0
Intra-Camera Supervised Person Re-Identification—0
How important are faces for person re-identification?—0
Illumination adaptive person reid based on teacher-student model and adversarial training—0
How Image Generation Helps Visible-to-Infrared Person Re-Identification?—0
Illumination Variation Correction Using Image Synthesis For Unsupervised Domain Adaptive Person Re-Identification—0
HorNet: A Hierarchical Offshoot Recurrent Network for Improving Person Re-ID via Image Captioning—0
Image-based human re-identification: Which covariates are actually (the most) important?—0
Deep Learning Prototype Domains for Person Re-Identification—0
Deep Attributes Driven Multi-Camera 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