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 551–575 of 1488 papers

TitleStatusHype
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id—0
Dense Interaction Learning for Video-based Person Re-identification—0
Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning—0
A Person Re-Identification System For Mobile Devices—0
Deep Transfer Metric Learning—0
Deep Transfer Learning for Person Re-identification—0
Bridging the Distribution Gap of Visible-Infrared Person Re-identification with Modality Batch Normalization—0
Adversarial Camera Alignment Network for Unsupervised Cross-camera Person Re-identification—0
Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification—0
Deep Self-Paced Learning for Person Re-Identification—0
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification—0
Deep Representation Learning with Part Loss for Person Re-Identification—0
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective—0
Bregman Divergences for Infinite Dimensional Covariance Matrices—0
A Pedestrian is Worth One Prompt: Towards Language Guidance Person Re-Identification—0
Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles—0
Deep Reinforcement Learning Attention Selection for Person Re-Identification—0
Branch-Cooperative OSNet for Person Re-Identification—0
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification—0
DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification—0
Anti-Forgetting Adaptation for Unsupervised Person Re-identification—0
Adversarial Binary Coding for Efficient Person Re-identification—0
Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach—0
Deep Ranking Model by Large Adaptive Margin Learning for Person Re-identification—0
Deep Ranking for Person Re-identification via Joint Representation Learning—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
10TransReID-SSL (ViT-B w/o 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
5RGT&RGPR (RK)mAP92.7—Unverified
6st-ReID(RE, RK,Cam)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