activity
20182022
most citedJoint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification

15 citations · 34 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV20226 cited

Federated and Generalized Person Re-identification through Domain and Feature Hallucinating

Fengxiang Yang, Zhun Zhong, Zhiming Luo +2

In this paper, we study the problem of federated domain generalization (FedDG) for person re-identification (re-ID), which aims to learn a generalized model with multiple decentral…

cs.CV202115 cited

Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification

Fengxiang Yang, Zhun Zhong, Zhiming Luo +4

This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data. One popular method is to obtain ps…

cs.CV2020

Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification

Yuyang Zhao, Zhun Zhong, Fengxiang Yang +4

Recent advances in person re-identification (ReID) obtain impressive accuracy in the supervised and unsupervised learning settings. However, most of the existing methods need to tr…

cs.CV201913 cited

Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification

Fengxiang Yang, Ke Li, Zhun Zhong +7

Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achi…

cs.CV2018

Leveraging Virtual and Real Person for Unsupervised Person Re-identification

Fengxiang Yang, Zhun Zhong, Zhiming Luo +2

Person re-identification (re-ID) is a challenging problem especially when no labels are available for training. Although recent deep re-ID methods have achieved great improvement,…