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 601–625 of 1488 papers

TitleStatusHype
Deep learning-based person re-identification methods: A survey and outlook of recent works—0
A Convolutional Baseline for Person Re-Identification Using Vision and Language Descriptions—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
Inserting Videos into Videos—0
Intra-Camera Supervised Person Re-Identification—0
Is Synthetic Dataset Reliable for Benchmarking Generalizable Person Re-Identification?—0
Joint Person Re-identification and Camera Network Topology Inference in Multiple Cameras—0
Deep Hybrid Similarity Learning for Person Re-identification—0
Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection—0
Improving Description-based Person Re-identification by Multi-granularity Image-text Alignments—0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
Improving One-shot NAS by Suppressing the Posterior Fading—0
Deep Similarity Metric Learning for Real-Time Pedestrian Tracking—0
Deep Feature Learning with Relative Distance Comparison for Person Re-identification—0
Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification—0
ANL: Anti-Noise Learning for Cross-Domain 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
Human Re-identification by Matching Compositional Template with Cluster Sampling—0
Beyond Augmentation: Empowering Model Robustness under Extreme Capture Environments—0
A Deep Structure of Person Re-Identification using Multi-Level Gaussian Models—0
Human-In-The-Loop Person Re-Identification—0
An Introduction to Person Re-identification with Generative Adversarial Networks—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