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Person Re Identification On Cuhk03

Metrics

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Results

Performance results of various models on this benchmark

Paper Title
AlignedReID (RK)97.899.899.6AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
Deep Constrained Dominant Sets95.8-99.1Deep Constrained Dominant Sets for Person Re-identification
FD-GAN92.6--FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
ProNet++ (ResNet50+RK)90.6--Rethinking Person Re-identification from a Projection-on-Prototypes Perspective
TriNet89.63-99.01In Defense of the Triplet Loss for Person Re-Identification
ProNet++85.2--Rethinking Person Re-identification from a Projection-on-Prototypes Perspective
Weakly Supervised Pre-training (ResNet50+BDB)84.7--Large-Scale Pre-training for Person Re-identification with Noisy Labels
VI+LSRO 384.6--Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
DLCE83.4--A Discriminatively Learned CNN Embedding for Person Re-identification
Unsupervised Pre-training (ResNet50+BDB)81.9--Unsupervised Pre-training for Person Re-identification
OIM Loss 4577.5--Joint Detection and Identification Feature Learning for Person Search
DG-Net65.6--Joint Discriminative and Generative Learning for Person Re-identification
TriNet + Era + Reranking (ACNet, bs=32)64.8--Adaptively Connected Neural Networks
k-reciprocal 4661.6--Re-ranking Person Re-identification with k-reciprocal Encoding
UTAL56.3--Unsupervised Tracklet Person Re-Identification
HA-CNN41.7--Harmonious Attention Network for Person Re-Identification
OSNet---Omni-Scale Feature Learning for Person Re-Identification
UniHCP (finetune)---UniHCP: A Unified Model for Human-Centric Perceptions
Proposed SGGNN---Person Re-identification with Deep Similarity-Guided Graph Neural Network
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Person Re Identification On Cuhk03 | SOTA | HyperAI