Generalizable Person Re-identification

5 papers with code • 6 benchmarks • 3 datasets

Generalizable person re-identification refers to methods trained on a source dataset but directly evaluated on a target dataset without domain adaptation or transfer learning.

Greatest papers with code

Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification

shengcailiao/QAConv 4 Apr 2021

Then, each mini batch is composed of a randomly selected class and its nearest neighboring classes so as to provide informative and challenging examples for learning.

Generalizable Person Re-identification Graph Sampling +3

Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification

VideoObjectSearch/RandPerson 23 Jun 2020

To address this, we propose to automatically synthesize a large-scale person re-identification dataset following a set-up similar to real surveillance but with virtual environments, and then use the synthesized person images to train a generalizable person re-identification model.

Domain Generalization Generalizable Person Re-identification +1

Meta Batch-Instance Normalization for Generalizable Person Re-Identification

bismex/MetaBIN 30 Nov 2020

To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers.

Data Augmentation Domain Generalization +2

Dual Distribution Alignment Network for Generalizable Person Re-Identification

Pealing/DDAN 27 Jul 2020

Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID), which trains the model using labels from the source domain alone, and then directly adopts the trained model to the target domain without model updating.

Domain Generalization Generalizable Person Re-identification