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 1251–1300 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
Weighted Bilinear Coding over Salient Body Parts for Person Re-identification—0
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal PatternsCode0
Virtual CNN Branching: Efficient Feature Ensemble for Person Re-IdentificationCode0
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking—0
A framework with updateable joint images re-ranking for Person Re-identification—0
Multi-Channel Pyramid Person Matching Network for Person Re-Identification—0
Pyramid Person Matching Network for Person Re-identification—0
Harmonious Attention Network for Person Re-IdentificationCode0
Video Person Re-identification by Temporal Residual Learning—0
Triplet-based Deep Similarity Learning for Person Re-IdentificationCode0
Multi-pseudo Regularized Label for Generated Data in Person Re-Identification—0
Re-ID done right: towards good practices for person re-identification—0
MobileNetV2: Inverted Residuals and Linear BottlenecksCode1
Crossing Generative Adversarial Networks for Cross-View Person Re-identification—0
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free ApproachCode0
Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project—0
LVreID: Person Re-Identification with Long Sequence Videos—0
Hierarchical Cross Network for Person Re-identification—0
Disentangled Person Image GenerationCode0
Pose-Normalized Image Generation for Person Re-identificationCode2
Adversarial Attribute-Image Person Re-identification—0
Distance-based Camera Network Topology Inference for Person Re-identification—0
Deep-Person: Learning Discriminative Deep Features for Person Re-IdentificationCode0
A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-RankingCode0
Camera Style Adaptation 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