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Zhitong Gao 郜之桐
First-Year PhD Student

About Me

Hi there! I am a first-year PhD student at EPFL's VILab, under the mentorship of Prof. Amir Zamir. Previously, I completed my master's studies at ShanghaiTech University PLUS Lab, , where I had the privilege of working under the mentorship of Prof. Xuming He. I also had the wonderful opportunity to spend time as a visiting student at EPFL's CVLab, working with Dr. Mathieu Salzmann.

My research interests broadly include Computer Vision and Deep Learning, with a focus on constructing robust and safe structure-prediction models under uncertainty. In pursuit of this goal, my current research work involves tackling learning problems such as learning with noisy labels, aleatoric uncertainty estimation, out-of-distribution detection, domain generalization and test-time adaptation.

I am always open to collaborating, please feel free to reach out if you share similar interests or want to discuss on relevant topics. 😊

News

  • 08/2024: I will begin my PhD studies at EPFL. Looking forward to this new adventure!
  • 07/2024: I served as a reviewer for NeurIPS 2024, reviewed 4 papers.
  • 05/2024: I completed my master's studies and successfully defended my thesis titled "Uncertainty Modeling and Its Applications in Semantic Segmentation."
  • 12/2023: I attended the NeurIPS 2023 in New Orleans and gave a poster presentation.
  • 11/2023: I received the China National Scholarship (rate 0.2%)!
  • 11/2023: One paper was accepted by ML4H 2023. Congrats to Bingnan!
  • 10/2023: I gave both an oral and a poster presentation at the ICCV 2023 UNCV Workshop in Paris.
  • 09/2023: One paper was accepted by NeurIPS 2023.
  • 09/2023: I have joined the EPFL CV lab as a visiting scholar, working with Mathieu Salzmann.
  • 08/2023: I served as a reviewer for NeurIPS 2023, reviewed 2 papers.
  • 08/2023: I attended the IJCAI conference onsite in Macau and gave both an oral and a poster presentation.
  • 05/2023: I attended the ICLR conference onsite in Rwanda and gave a poster presentation.
  • 04/2023: One paper was accepted by IJCAI 2023. Congrats to Chuanyang!
  • 01/2023: I served as a reviewer for CVPR 2023, reviewed 2 papers.
  • 01/2023: One paper was accepted by ICLR 2023.
  • 10/2021: I attended the MICCAI conference online and gave a poster presentation.
  • 07/2021: I finished my undergraduate studies and received an outstanding undergraduate thesis award!
  • 05/2021: One paper was accepted by MICCAI 2021.

Publications

ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribution Detection in Segmentation
[PDF] [Code] [Poster] [Slides]
Zhitong Gao, Shipeng Yan, Xuming He
Conference on Neural Information Processing Systems (NeurIPS), 2023
Out-of-Distribution Detection Anomaly Segmentation Test-Time Adaptation
Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI Segmentation
[PDF] [Code]
Bingnan Li, Zhitong Gao, Xuming He
Machine Learning for Health (ML4H), 2023
Domain Generalization Test-Time Adaptation Medical Image Segmentation
Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts
[PDF] [Code] [Poster] [Video]
Zhitong Gao, Yucong Chen, Chuyu Zhang, Xuming He
International Conference on Learning Representations (ICLR), 2023
Aleatoric Uncertainty Estimation Stochastic Segmentation Generative Model
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels
[PDF] [Code]
Chuanyang Hu, Shipeng Yan, Zhitong Gao, Xuming He
International Joint Conference on Artificial Intelligence (IJCAI), 2023
Robust learning Learning Dynamics
Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation
[PDF] [Code] [Poster] [Video]
Shuailin Li*, Zhitong Gao*, Xuming He
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021
Robust learning Medical Image Segmentation
Note: * indicates equal contribution.

Challenges and Workshops

MUAD Uncertainty Estimation for Semantic Segmentation Challenge at ICCV2023
[Challenge Website] [Report] [Slides]
Zhitong Gao, Xuming He
Top 7; Invited for oral and poster presentation at ICCV UNCV Workshop.
Out-of-distribution Detection Uncertainty Estimation Test-Time Adaptation
Quantification of Uncertainties in Biomedical Image Quantification Challenge at MICCAI2021
[Challenge Website] [Report] [Poster]
Yucong Chen, Guanqi He, Zhitong Gao, Xuming He
Invited for oral and poster presentation at MICCAI 2021 Workshop.
Uncertainty Estimation Medical Image Segmentation

Course Projects

Understanding and Exploration for "Adaptive Online Learning in Dynamic Environments"
[Report]
Zhitong Gao*, Haotian Tian*, Tianyue Zhou*
Final Project of CS245 Online Optimization and Learning, 2023 Spring, ShanghaiTech University
Online Learning Optimization Non-stationary Environment
Generalized DUQ: Generalized Deterministic Uncertainty Quantification
[Report]
Zhitong Gao*, Ziyao Zeng*
Final Project of CS282 Machine Learning, 2021 Spring, ShanghaiTech University
Uncertainty Quantification Aleatoric Uncertainty Epistemic Uncertainty
Seek Common while Shelving Differences: A New Way for dealing with Noisy Labels
[Report]
Zhitong Gao*, Ziyao Zeng*
Final Project of CS280 Deep Learning, 2020 Fall, ShanghaiTech University
Robust Learning Noisy label

Education

Ph.D. in Computer and Communication Sciences (EDIC), EPFL, Lausanne, Switzerland
Sep. 2024 - Now
M.Sc. in School of Information Science and Technology, ShanghaiTech University, Shanghai, China
Sep. 2021 - Jul. 2024
B.E. in School of Information Science and Technology, ShanghaiTech University, Shanghai, China
Sep. 2017 - Jul. 2021

Services

  • Reviewer of CVPR 2023, NeurIPS 2023, CVPR 2024, ECCV 2024, NeurIPS 2024
  • Teaching Assistant of CS280 Deep Learning, 2022 Fall, ShanghaiTech University
Copyright © Zhitong Gao 2023 (Thanks to Shuaiyi Huang)

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