News
[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.
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Research
I'm interested in Machine Learning, Bayesian Models, and Meta-Learning. Much of my research is to address challenging problems in multi-task learning.
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Episodic Multi-Task Learning with Heterogeneous Neural Processes
Jiayi Shen,
Xiantong Zhen,
Cheems Wang,
Marcel Worring
NeurIPS, 2023, Spotlight
paper(coming soon) /
code(coming soon)
This paper focuses on the data-insufficiency problem in multi-task learning within an episodic training set-up.
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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.
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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.
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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.
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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.
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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.
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