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 1101–1150 of 1488 papers

TitleStatusHype
Deep Hybrid Similarity Learning for Person Re-identification—0
Deep Learning-based Occluded Person Re-identification: A Survey—0
Deep Learning based Person Re-identification—0
Deep learning-based person re-identification methods: A survey and outlook of recent works—0
Deep Learning for Video-based Person Re-Identification: A Survey—0
Deep Learning Prototype Domains for Person Re-Identification—0
Deep Linear Discriminant Analysis on Fisher Networks: A Hybrid Architecture for Person Re-identification—0
Deep Metric Learning for Practical Person Re-Identification—0
Deep Multi-Index Hashing for Person Re-Identification—0
Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking—0
DeepPFCN: Deep Parallel Feature Consensus Network For Person Re-Identification—0
Deep Ranking for Person Re-identification via Joint Representation Learning—0
Deep Ranking Model by Large Adaptive Margin Learning for Person Re-identification—0
Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach—0
DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification—0
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification—0
Deep Reinforcement Learning Attention Selection for Person Re-Identification—0
Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles—0
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective—0
Deep Representation Learning with Part Loss for Person Re-Identification—0
Deep Self-Paced Learning for Person Re-Identification—0
Deep Transfer Learning for Person Re-identification—0
Deep Transfer Metric Learning—0
Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning—0
Dense Interaction Learning for Video-based Person Re-identification—0
Densely Semantically Aligned Person Re-Identification—0
Density-Adaptive Kernel based Efficient Reranking Approaches for Person Reidentification—0
DeSPITE: Exploring Contrastive Deep Skeleton-Pointcloud-IMU-Text Embeddings for Advanced Point Cloud Human Activity Understanding—0
Devil's in the Details: Aligning Visual Clues for Conditional Embedding in Person Re-Identification—0
DEX: Domain Embedding Expansion for Generalized Person Re-identification—0
DF^2AM: Dual-level Feature Fusion and Affinity Modeling for RGB-Infrared Cross-modality Person Re-identification—0
Differentially Private Cross-camera Person Re-identification—0
DiffPhysBA: Diffusion-based Physical Backdoor Attack against Person Re-Identification in Real-World—0
Discrepant and Multi-Instance Proxies for Unsupervised Person Re-Identification—0
Discriminative Feature Learning with Foreground Attention for Person Re-Identification—0
Discriminative Feature Learning With Consistent Attention Regularization for Person Re-Identification—0
Discriminative Pedestrian Features and Gated Channel Attention for Clothes-Changing Person Re-Identification—0
Disentangled Representations for Short-Term and Long-Term Person Re-Identification—0
Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification—0
Distance-based Camera Network Topology Inference for Person Re-identification—0
Distilled Person Re-Identification: Towards a More Scalable System—0
Distilling CLIP with Dual Guidance for Learning Discriminative Human Body Shape Representation—0
Distribution Context Aware Loss for Person Re-identification—0
Diverse Deep Feature Ensemble Learning for Omni-Domain Generalized Person Re-identification—0
Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer—0
Diverse Semantics-Guided Feature Alignment and Decoupling for Visible-Infrared Person Re-Identification—0
Diversity-Achieving Slow-DropBlock Network for Person Re-Identification—0
Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification—0
Divide and Fuse: A Re-ranking Approach for Person Re-identification—0
DMRNet++: Learning Discriminative Features with Decoupled Networks and Enriched Pairs for One-Step Person Search—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