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
Temporal Continuity Based Unsupervised Learning for Person Re-Identification—0
Temporal Model Adaptation for Person Re-Identification—0
TextAug: Test time Text Augmentation for Multimodal Person Re-identification—0
The Arm-Swing Is Discriminative in Video Gait Recognition for Athlete Re-Identification—0
The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching—0
The Group Loss++: A deeper look into group loss for deep metric learning—0
The Multi-Modal Video Reasoning and Analyzing Competition—0
Three-Stream Convolutional Networks for Video-based Person Re-Identification—0
Time-Frequency Analysis of Variable-Length WiFi CSI Signals for Person Re-Identification—0
TOPLight: Lightweight Neural Networks With Task-Oriented Pretraining for Visible-Infrared Recognition—0
Top-push Video-based Person Re-identification—0
Towards Discriminative Representation Learning for Unsupervised Person Re-identification—0
Towards Egocentric Person Re-identification and Social Pattern Analysis—0
Towards Fewer Labels: Support Pair Active Learning for Person Re-identification—0
Towards Generalizable Person Re-identification with a Bi-stream Generative Model—0
Towards Homogeneous Modality Learning and Multi-Granularity Information Exploration for Visible-Infrared Person Re-Identification—0
Towards Practical Implementations of Person Re-Identification from Full Video Frames—0
Towards Precise Intra-camera Supervised Person Re-identification—0
Towards Privacy-Preserving Person Re-identification via Person Identify Shift—0
Towards Rich Feature Discovery With Class Activation Maps Augmentation for Person Re-Identification—0
Towards Visually Explaining Similarity Models—0
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking—0
Tran-GCN: A Transformer-Enhanced Graph Convolutional Network for Person Re-Identification in Monitoring Videos—0
Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification—0
Transferring a Semantic Representation for Person Re-Identification and Search—0
Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification—0
TransitReID: Transit OD Data Collection with Occlusion-Resistant Dynamic Passenger Re-Identification—0
Triplet Online Instance Matching Loss for Person Re-identification—0
TSDW: A Tri-Stream Dynamic Weight Network for Cloth-Changing Person Re-Identification—0
TVPR: Text-to-Video Person Retrieval and a New Benchmark—0
Unified Batch All Triplet Loss for Visible-Infrared Person Re-identification—0
Unified Framework for Automated Person Re-identification and Camera Network Topology Inference in Camera Networks—0
Unified Multifaceted Feature Learning for Person Re-Identification—0
Unified Representation Learning for Cross Model Compatibility—0
Unity Style Transfer for Person Re-Identification—0
Beyond Universal Person Re-ID Attack—0
Universal Person Re-Identification—0
Unleashing Potential of Unsupervised Pre-Training With Intra-Identity Regularization for Person Re-Identification—0
Unleashing the Potential of Tracklets for Unsupervised Video Person Re-Identification—0
Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks—0
Unsupervised Clustering Active Learning for Person Re-identification—0
Fully Unsupervised Person Re-identification viaSelective Contrastive Learning—0
Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification—0
Unsupervised Deep Metric Learning via Auxiliary Rotation Loss—0
Unsupervised Disentanglement GAN for Domain Adaptive Person Re-Identification—0
Unsupervised Domain Adaptation for Cross-Regional Scenes Person Re-identification—0
Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling—0
Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis—0
Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning—0
Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for 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