Search Results for author: Zhun Zhong

Found 20 papers, 12 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

Transformer-Based Source-Free Domain Adaptation

2 code implementations28 May 2021 Guanglei Yang, Hao Tang, Zhun Zhong, Mingli Ding, Ling Shao, Nicu Sebe, Elisa Ricci

In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation.

Domain Adaptation Knowledge Distillation

Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation

1 code implementation CVPR 2021 Subhankar Roy, Evgeny Krivosheev, Zhun Zhong, Nicu Sebe, Elisa Ricci

In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains.

Curriculum Learning Domain Adaptation +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

Generalizing A Person Retrieval Model Hetero- and Homogeneously

1 code implementation ECCV 2018 Zhun Zhong, Liang Zheng, Shaozi Li, Yi Yang

Person re-identification (re-ID) poses unique challenges for unsupervised domain adaptation (UDA) in that classes in the source and target sets (domains) are entirely different and that image variations are largely caused by cameras.

Person Re-Identification Person Retrieval +1

Random Erasing Data Augmentation

14 code implementations16 Aug 2017 Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, Yi Yang

In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).

General Classification Image Augmentation +3

Re-ranking Person Re-identification with k-reciprocal Encoding

no code implementations CVPR 2017 Zhun Zhong, Liang Zheng, Donglin Cao, Shaozi Li

Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance.

Person Re-Identification Re-Ranking

Re-ranking Object Proposals for Object Detection in Automatic Driving

no code implementations19 May 2016 Zhun Zhong, Mingyi Lei, Shaozi Li, Jianping Fan

In this paper, we propose a semantic, class-specific approach to re-rank object proposals, which can consistently improve the recall performance even with less proposals.

Object Detection Re-Ranking +1

Detecting Ground Control Points via Convolutional Neural Network for Stereo Matching

no code implementations8 May 2016 Zhun Zhong, Songzhi Su, Donglin Cao, Shaozi Li

Secondly, we present a ground control points selection scheme according to the maximum matching confidence of each pixel.

Stereo Matching Stereo Matching Hand

Unsupervised domain adaption dictionary learning for visual recognition

no code implementations3 Jun 2015 Zhun Zhong, Zongmin Li, Runlin Li, Xiaoxia Sun

However, when the data instances of a target domain have a different distribution than that of a source domain, the dictionary learning method may fail to perform well.

Dictionary Learning Domain Adaptation

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