55 citations · 148 across the 10 of their papers we have counts for
10 papers · 1 filter
Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization
Yingxin Lai, Zhiming Luo, Zitong Yu
The rapid advancements in computer vision have stimulated remarkable progress in face forgery techniques, capturing the dedicated attention of researchers committed to detecting fo…
Source-Free Open Compound Domain Adaptation in Semantic Segmentation
Yuyang Zhao, Zhun Zhong, Zhiming Luo +2
In this work, we introduce a new concept, named source-free open compound domain adaptation (SF-OCDA), and study it in semantic segmentation. SF-OCDA is more challenging than the t…
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…
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…
OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World
Zhun Zhong, Linchao Zhu, Zhiming Luo +3
In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes. Existing methods typically first pre-train a mode…
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…
Learning to Adapt Invariance in Memory for Person Re-identification
Zhun Zhong, Liang Zheng, Zhiming Luo +2
This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain.…
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
Zhun Zhong, Liang Zheng, Zhiming Luo +2
This paper considers the domain adaptive person re-identification (re-ID) problem: learning a re-ID model from a labeled source domain and an unlabeled target domain. Conventional…
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,…
GridNet with automatic shape prior registration for automatic MRI cardiac segmentation
Clement Zotti, Zhiming Luo, Alain Lalande +2
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. Th…