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 676–700 of 1488 papers

TitleStatusHype
Cross-Modal Distillation for RGB-Depth Person Re-Identification—0
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification—0
Inserting Videos into Videos—0
Hierarchical Invariant Feature Learning with Marginalization for Person Re-Identification—0
Deep Active Learning for Video-based Person Re-identification—0
Integrating Coarse Granularity Part-level Features with Supervised Global-level Features for Person Re-identification—0
Deep Ranking for Person Re-identification via Joint Representation Learning—0
Learning a Discriminative Null Space for Person Re-identification—0
Inter-Task Association Critic for Cross-Resolution Person Re-Identification—0
Intra-Camera Supervised Person Re-Identification: A New Benchmark—0
Intra-Camera Supervised Person Re-Identification—0
Learning Comprehensive Representations with Richer Self for Text-to-Image Person Re-Identification—0
Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals—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
iQIYI-VID: A Large Dataset for Multi-modal Person Identification—0
Is Synthetic Dataset Reliable for Benchmarking Generalizable Person Re-Identification?—0
Iterated Support Vector Machines for Distance Metric Learning—0
Hierarchical Gaussian Descriptors with Application to Person Re-Identification—0
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective—0
Hierarchical Gaussian Descriptor for Person Re-Identification—0
Joint Discriminative and Metric Embedding Learning for Person Re-Identification—0
Decentralised Person Re-Identification with Selective Knowledge Aggregation—0
Hierarchical Cross Network for Person Re-identification—0
Hierarchical Clustering with Hard-batch Triplet Loss 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