Jiayi Shen (沈佳怡)

I am a final year ELLIS PhD candidate at MultiX Lab, University of Amsterdam. I am grateful to be supervised by Prof. Dr. Marcel Worring and Dr. Nanne van Noord. I also received valuable guidance from Dr. Xiantong Zhen. Prior to that, I obtained my Master's and Bachelor's degrees from Beihang University.

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[Feb. 2024] One tutorial on "How to Improve Your Academic Reading Skills" in MultiX. Slides can be found HERE!

[Sep. 2023] One paper was accepted by NeurIPS 2023 as spotlight ⭐!

[June 2023] One paper was accepted by CoLLAs, 2023.

[April 2023] I will attend the ICVSS2023, hosted in Punta Sampieri - Scicli (Ragusa), Sicily 9-15 July 2023.

[April 2023] I am a reviewer of ICCV2023 and Neurips2023.

[March 2023] I just finished my 36-month evaluation.

[March 2023] One paper was accepted by CVPR 2023.


I'm interested in Machine Learning, Bayesian Models, and Meta-Learning. Much of my research is to address challenging problems in multi-task learning.

Episodic Multi-Task Learning with Heterogeneous Neural Processes
Jiayi Shen, Xiantong Zhen, Qi (Cheems) Wang, Marcel Worring
NeurIPS, 2023, Spotlight
paper / code

This paper focuses on the data-insufficiency problem in multi-task learning within an episodic training set-up.

SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail
Yingjun Du, Jiayi Shen, Xiantong Zhen, Cees Snoek
CVPR, 2023
paper / code

We propose SuperDisco to discover super-class representations for long-tailed recognition using a graph model.

Association Graph Learning for Multi-Task Classification with Category Shifts
Jiayi Shen, Zehao Xiao, Xiantong Zhen, Cees Snoek, Marcel Worring
NeurIPS, 2022
paper / code

We propose a new MTL setting which suffers from category shifts from training to test data.

NFormer: Robust Person Re-identification with Neighbor Transformer
Haochen Wang, Jiayi Shen, Yongtuo Liu, Yan Gao (Xiaohongshu Inc.), Efstratios Gavves
CVPR, 2022
paper / code

We propose a Neighbor Transformer Network, or NFormer, which explicitly models interactions across all input images.

Variational Multi-Task Learning with Gumbel-Softmax Priors
Jiayi Shen, Xiantong Zhen, Marcel Worring, Ling Shao
NeurIPS, 2021
paper / code

We propose variational multi-task learning (VMTL), a general probabilistic inference framework for learning multiple related tasks.

A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
Zehao Xiao, Jiayi Shen, Xiantong Zhen, Ling Shao, Cees Snoek
ICML, 2021
paper / code

To better explore domain invariant learning, we introduce weight uncertainty to the model by leveraging variational Bayesian inference.

This website is based on Jon's source code.