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 1251–1275 of 1488 papers

TitleStatusHype
Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identificationCode0
Domain Adaptation through Synthesis for Unsupervised Person Re-identification—0
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification—0
Homocentric Hypersphere Feature Embedding for Person Re-identification—0
Part-Aligned Bilinear Representations for Person Re-identification—0
Horizontal Pyramid Matching for Person Re-identificationCode0
Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification—0
MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification—0
Recurrent Neural Networks for Person Re-identification Revisited—0
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human ParsingCode0
Occluded Person Re-identification—0
Learning Discriminative Features with Multiple Granularities for Person Re-IdentificationCode0
Graph Correspondence Transfer for Person Re-identification—0
Human Semantic Parsing for Person Re-identification—0
Learning View-Specific Deep Networks for Person Re-Identification—0
Efficient and Deep Person Re-Identification using Multi-Level Similarity—0
Adversarial Binary Coding for Efficient Person Re-identification—0
Features for Multi-Target Multi-Camera Tracking and Re-Identification—0
Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification—0
Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification—0
Person re-identification with fusion of hand-crafted and deep pose-based body region features—0
Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification—0
Domain transfer convolutional attribute embedding—0
Multi-Level Factorisation Net for Person Re-Identification—0
Pose-Driven Deep Models for Person Re-IdentificationCode0
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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