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 901–925 of 1488 papers

TitleStatusHype
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
When Large Vision-Language Models Meet Person Re-Identification—0
When Person Re-identification Meets Changing Clothes—0
Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification—0
Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition—0
WhoFi: Deep Person Re-Identification via Wi-Fi Channel Signal Encoding—0
Wide-Baseline Multi-Camera Calibration using Person Re-Identification—0
WRIM-Net: Wide-Ranging Information Mining Network for Visible-Infrared Person Re-Identification—0
X-ReID: Cross-Instance Transformer for Identity-Level Person Re-Identification—0
YOLORe-IDNet: An Efficient Multi-Camera System for Person-Tracking—0
Temporal Coherence or Temporal Motion: Which is More Critical for Video-based Person Re-identification?—0
Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation—0
3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification—0
AAformer: Auto-Aligned Transformer for Person Re-Identification—0
AANet: Attribute Attention Network for Person Re-Identifications—0
AaP-ReID: Improved Attention-Aware Person Re-identification—0
A Brief Survey on Person Recognition at a Distance—0
Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling—0
A Convolutional Baseline for Person Re-Identification Using Vision and Language Descriptions—0
Cross-Modal Distillation for RGB-Depth Person Re-Identification—0
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification—0
Adaptive Cross-Domain Learning for Generalizable Person Re-Identification—0
Adaptive Deep Metric Embeddings for Person Re-Identification under Occlusions—0
Adaptive Generation of Privileged Intermediate Information for Visible-Infrared Person Re-Identification—0
Adaptive Intra-Class Variation Contrastive Learning for Unsupervised 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