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 851–900 of 1488 papers

TitleStatusHype
Set Augmented Triplet Loss for Video Person Re-Identification—0
Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning—0
Fully Unsupervised Person Re-identification viaSelective Contrastive Learning—0
Taking A Closer Look at Synthesis: Fine-grained Attribute Analysis for Person Re-Identification—0
Police-In-The-Loop Person Re-identification—0
Integrating Coarse Granularity Part-level Features with Supervised Global-level Features for Person Re-identification—0
How important are faces for person re-identification?—0
Domain Agnostic Learning for Unbiased Authentication—0
End-to-End Training of CNN Ensembles for Person Re-Identification—0
Semantics-Guided Clustering with Deep Progressive Learning for Semi-Supervised Person Re-identification—0
Taking Modality-free Human Identification as Zero-shot Learning—0
Concentrated Multi-Grained Multi-Attention Network for Video Based Person Re-Identification—0
FTN: Foreground-Guided Texture-Focused Person Re-Identification—0
Beyond Triplet Loss: Person Re-identification with Fine-grained Difference-aware Pairwise Loss—0
Batch Coherence-Driven Network for Part-aware Person Re-Identification—0
Improving Person Re-identification with Iterative Impression Aggregation—0
Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling—0
Robust Person Re-Identification through Contextual Mutual Boosting—0
SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification—0
Devil's in the Details: Aligning Visual Clues for Conditional Embedding in Person Re-Identification—0
Temporal Attribute-Appearance Learning Network for Video-based Person Re-Identification—0
Progressive Bilateral-Context Driven Model for Post-Processing Person Re-IdentificationCode0
Attribute Adaptive Margin Softmax Loss using Privileged Information—0
Class Interference Regularization—0
IAUnet: Global Context-Aware Feature Learning for Person Re-IdentificationCode0
Towards Practical Implementations of Person Re-Identification from Full Video Frames—0
Temporal Continuity Based Unsupervised Learning for Person Re-Identification—0
Receptive Multi-granularity Representation for Person Re-Identification—0
Learning to Learn in a Semi-Supervised Fashion—0
Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory—0
Black Re-ID: A Head-shoulder Descriptor for the Challenging Problem of Person Re-Identification—0
Person image generation with semantic attention network for person re-identification—0
Not 3D Re-ID: a Simple Single Stream 2D Convolution for Robust Video Re-identification—0
Apparel-invariant Feature Learning for Apparel-changed Person Re-identification—0
Towards Visually Explaining Similarity Models—0
Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds—0
Unified Representation Learning for Cross Model Compatibility—0
Incomplete Descriptor Mining with Elastic Loss for Person Re-Identification—0
A Flow-Guided Mutual Attention Network for Video-Based Person Re-Identification—0
Hierarchical Bi-Directional Feature Perception Network for Person Re-Identification—0
Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-IdentificationCode0
Temporal Coherence or Temporal Motion: Which is More Critical for Video-based Person Re-identification?—0
An Attention-driven Two-stage Clustering Method for Unsupervised Person Re-Identification—0
Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification—0
Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification—0
READ: Reciprocal Attention Discriminator for Image-to-Video Re-IdentificationCode0
Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification—0
Unsupervised Disentanglement GAN for Domain Adaptive Person Re-Identification—0
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning—0
Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive 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
10TransReID-SSL (ViT-B w/o 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
5RGT&RGPR (RK)mAP92.7—Unverified
6st-ReID(RE, RK,Cam)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