Yongxing Dai

I am a fourth year Ph.D student at the National Engineering Laboratory for Video Technology (NELVT), School of Electrical Engineering and Computer Science, Peking University, supervised by Prof. Ling-Yu Duan. In 2018, I obtained my B.Eng. degree in computer science and technology from Xidian University, Xi’an, China.

My current research focuses on computer vision and trasnfer learning, especially person re-identification, domain adaptation, and domain generalization.

Email  /  Google Scholar  /  Github

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2021-09: One paper is accepted by NeurIPS 2021.

2021-07: One paper is accepted by ICCV 2021 and one paper is accepted by TIP.


Digraph Contrastive Learning
Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, Changhu Wang,
NeurIPS, 2021
paper / code / bibtex

We design a digraph data augmentation method called Laplacian perturbation and theoretically analyze how it provides contrastive information without changing the digraph structure.

IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID
Yongxing Dai, Jun Liu, Yifan Sun, Zekun Tong, Chi Zhang, Ling-Yu Duan
ICCV, 2021   (Oral Presentation)
paper / arXiv / code / bibtex

IDM is a plug-and-play module to generate intermediate domains' representations, which can bridge source and target domains in unsupervised domain adaptive person re-ID.

Dual-Refinement: Joint Label and Feature Refinement for Unsupervised Domain Adaptive Person Re-Identification
Yongxing Dai, Jun Liu, Yan Bai, Zekun Tong, Ling-Yu Duan
IEEE Transactions on Image Processing (TIP), 2021  
paper / arXiv / code / bibtex

Dual-Refinement can jointly refine pseudo labels at the off-line clustering phase and features at the on-line training phase, to alternatively boost the label purity and feature discriminability in the target domain for more reliable re-ID.

Generalizable Person Re-identification with Relevance-aware Mixture of Experts
Yongxing Dai, Xiaotong Li, Jun Liu, Zekun Tong, Ling-Yu Duan
CVPR, 2021  
paper / arXiv / bibtex

We propose a novel method called the relevance-aware mixture of experts (RaMoE), using an effective voting-based mixture mechanism to dynamically leverage source domains’ diverse characteristics to improve the model’s generalization

Disentangled Feature Learning Network for Vehicle Re-Identification
Yan Bai, Yihang Lou, Yongxing Dai, Jun Liu, Ziqian Chen, Ling-Yu Duan
IJCAI, 2020  
paper / bibtex

We propose a Disentangled Feature Learning Network (DFLNet) to learn orientation specific and common features concurrently. Moreover, to effectively use these two types of features for ReID, we further design a feature metric alignment scheme to ensure the consistency of the metric scales.


Peking University President Scholarship     2021-2022

Academic Services

Journal Reviewer: TMM, Neurocomputing

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