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

Showing 12011225 of 1488 papers

TitleStatusHype
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-IdentificationCode0
Unsupervised Person Re-identification by Deep Learning Tracklet Association0
Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-identification0
Generalizing A Person Retrieval Model Hetero- and HomogeneouslyCode0
Robust Anchor Embedding for Unsupervised Video Person Re-Identification in the Wild0
Deep Association Learning for Unsupervised Video Person Re-identificationCode0
Person Re-Identification by Semantic Region Representation and Topology Constraint0
Incremental Learning in Person Re-IdentificationCode0
Support Neighbor Loss for Person Re-IdentificationCode0
Measuring the Temporal Behavior of Real-World Person Re-Identification0
Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association0
Multi-shot Person Re-identification through Set Distance with Visual Distributional Representation0
Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification0
Deep Group-shuffling Random Walk for Person Re-identificationCode0
End-to-End Deep Kronecker-Product Matching for Person Re-identificationCode0
Hard-Aware Point-to-Set Deep Metric for Person Re-identificationCode0
Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identificationCode0
Maximum Margin Metric Learning Over Discriminative Nullspace for Person Re-identification0
Person Search in Videos with One Portrait Through Visual and Temporal LinksCode0
Adversarial Open-World Person Re-Identification0
Person Re-identification with Deep Similarity-Guided Graph Neural Network0
Person re-identification across different datasets with multi-task learning0
Partial Person Re-identification with Alignment and Hallucination0
Improving Deep Models of Person Re-identification for Cross-Dataset Usage0
Person Search via A Mask-Guided Two-Stream CNN Model0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1st-ReID(RE, RK)Rank-198Unverified
2SSKD(GH)Rank-197.36Unverified
3CLIP-ReID+Pose2ID (no RK)Rank-197.3Unverified
4SOLIDER +UFFM+AMCRank-197Unverified
5Unsupervised Pre-training (ResNet101+MGN)Rank-197Unverified
6RGT&RGPR (RK)Rank-196.9Unverified
7SOLIDERRank-196.9Unverified
8LightMBN (RR)Rank-196.8Unverified
9Viewpoint-Aware Loss(RK)Rank-196.79Unverified
10TransReID-SSL (ViT-B w/o RK)Rank-196.7Unverified
#ModelMetricClaimedVerifiedStatus
1DenseILmAP97.1Unverified
2CTL Model (ResNet50, 256x128)mAP96.1Unverified
3BPBreID (RK)mAP92.9Unverified
4Unsupervised Pre-training (ResNet101+RK)mAP92.77Unverified
5RGT&RGPR (RK)mAP92.7Unverified
6st-ReID(RE, RK,Cam)mAP92.7Unverified
7Viewpoint-Aware Loss(RK)mAP91.8Unverified
8LDS (ResNet50 + RK)mAP91Unverified
9Adaptive L2 Regularization (with re-ranking)mAP90.7Unverified
10FlipReID (with re-ranking)mAP90.7Unverified