Search Results for author: Zhiming Luo

Found 11 papers, 6 papers with code

Neighborhood Contrastive Learning for Novel Class Discovery

no code implementations CVPR 2021 Zhun Zhong, Enrico Fini, Subhankar Roy, Zhiming Luo, Elisa Ricci, Nicu Sebe

In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes.

Contrastive Learning

Source-Free Open Compound Domain Adaptation in Semantic Segmentation

no code implementations7 Jun 2021 Yuyang Zhao, Zhun Zhong, Zhiming Luo, Gim Hee Lee, Nicu Sebe

Second, CPSS can reduce the influence of noisy pseudo-labels and also avoid the model overfitting to the target domain during self-supervised learning, consistently boosting the performance on the target and open domains.

Domain Generalization Self-Supervised Learning +1

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

1 code implementation CVPR 2021 Yuyang Zhao, Zhun Zhong, Fengxiang Yang, Zhiming Luo, Yaojin Lin, Shaozi Li, Nicu Sebe

In this paper, we study the problem of multi-source domain generalization in ReID, which aims to learn a model that can perform well on unseen domains with only several labeled source domains.

Domain Generalization Meta-Learning +1

OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World

no code implementations CVPR 2021 Zhun Zhong, Linchao Zhu, Zhiming Luo, Shaozi Li, Yi Yang, Nicu Sebe

In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes.

Learning to Adapt Invariance in Memory for Person Re-identification

no code implementations1 Aug 2019 Zhun Zhong, Liang Zheng, Zhiming Luo, Shaozi Li, Yi Yang

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.

Person Re-Identification Unsupervised Domain Adaptation

Leveraging Virtual and Real Person for Unsupervised Person Re-identification

1 code implementation5 Nov 2018 Fengxiang Yang, Zhun Zhong, Zhiming Luo, Sheng Lian, Shaozi Li

For training of deep re-ID model, we divide it into three steps: 1) pre-training a coarse re-ID model by using virtual data; 2) collaborative filtering based positive pair mining from the real data; and 3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs and virtual data.

Collaborative Filtering Style Transfer +1

GridNet with automatic shape prior registration for automatic MRI cardiac segmentation

no code implementations24 May 2017 Clement Zotti, Zhiming Luo, Alain Lalande, Olivier Humbert, Pierre-Marc Jodoin

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.

Cardiac Segmentation Image Cropping

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