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 11011125 of 1488 papers

TitleStatusHype
Person Re-identification with Metric Learning using Privileged Information0
Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification0
Convolutional Temporal Attention Model for Video-based Person Re-identification0
Weakly Supervised Person Re-Identification0
Weakly Supervised Person Re-ID: Differentiable Graphical Learning and A New BenchmarkCode1
Adaptively Connected Neural NetworksCode0
Progressive Pose Attention Transfer for Person Image GenerationCode1
Re-Identification Supervised Texture Generation0
A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification0
Relation-Aware Global Attention for Person Re-identificationCode0
Learning Context Graph for Person Search0
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identificationCode0
Cross-Entropy Adversarial View Adaptation for Person Re-identification0
CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification0
Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identificationCode0
Pedestrian re-identification based on Tree branch network with local and global learning0
Person Re-identification with Bias-controlled Adversarial Training0
Few-Shot Deep Adversarial Learning for Video-based Person Re-identification0
GAN-based Pose-aware Regulation for Video-based Person Re-identification0
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-IdentificationCode0
Bag of Tricks and A Strong Baseline for Deep Person Re-identificationCode2
STNReID : Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification0
Unsupervised Person Re-identification by Soft Multilabel LearningCode0
Inserting Videos into Videos0
Learning Feature Aggregation in Temporal Domain for Re-Identification0
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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