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Person Re Identification On Cuhk03 Labeled

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MAP
Rank-1

Résultats

Résultats de performance de divers modèles sur ce benchmark

Paper Title
Weakly Pre-training (ResNet101+RK)89.2987.86-
Top-DB-Net + RK88.586.7Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification
DiP (without RK)85.787DiP: Learning Discriminative Implicit Parts for Person Re-Identification
LightMBN (w/o ReRank)85.187.2Lightweight Multi-Branch Network for Person Re-Identification
Deep Miner (w/o ReRank)84.786.6Deep Miner: A Deep and Multi-branch Network which Mines Rich and Diverse Features for Person Re-identification
FPB83.885.9FPB: Feature Pyramid Branch for Person Re-Identification
MPN (without re-ranking)81.185Multi-task Learning with Coarse Priors for Robust Part-aware Person Re-identification
PLR-OSNet80.584.6Learning Diverse Features with Part-Level Resolution for Person Re-Identification
Pyramid (CVPR' 19)76.978.9Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
BDB (ICCV'19)76.779.4Batch DropBlock Network for Person Re-identification and Beyond
Top-DB-Net75.479.4Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification
Auto-ReID (ICCV'19)73.077.9Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
MGN (ACM MM'18)67.468.0Learning Discriminative Features with Multiple Granularities for Person Re-Identification
DaRe+RE (CVPR'18)61.666.1Resource Aware Person Re-identification across Multiple Resolutions
PAN+re-rank45.843.9Pedestrian Alignment Network for Large-scale Person Re-identification
HA-CNN (CVPR'18)41.044.4Harmonious Attention Network for Person Re-Identification
PAN(Zheng et al., [2017a])35.036.9Pedestrian Alignment Network for Large-scale Person Re-identification
IDE-R+XQDA29.632.0Re-ranking Person Re-identification with k-reciprocal Encoding
IDE-R21.022.2Re-ranking Person Re-identification with k-reciprocal Encoding
IDE-C+XQDA20.021.9Re-ranking Person Re-identification with k-reciprocal Encoding
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Person Re Identification On Cuhk03 Labeled | SOTA | HyperAI