Kuku the cat, Yuanzhi Liang's post-training advisor
kuku — my post-training advisor; specializes in comfort priors, household physics, and alignment with human preferences.

Yuanzhi Liang

Mail: liangyzh18 [at] outlook [dot] com

Profiles: ORCID · DBLP · Google Scholar

Bilibili icon Zhihu icon Xiaohongshu icon

About Me

I am a research scientist specializing in generative AI at the Institute of Artificial Intelligence (TeleAI), China Telecom. I received my Ph.D. from the University of Technology Sydney in 2024, advised by Dr. Linchao Zhu and Prof. Yi Yang.

I received a Master's degree from Xi'an Jiaotong University in 2020 and was a member of the SMILES LAB, advised by Prof. Xueming Qian and Prof. Li Zhu.

My research focuses on visual learning systems that generalize beyond fixed training distributions and continue to improve after pre-training. The work studies both the content of learned representations—discriminative visual features, semantic relationships, temporal dependencies, and scene state—and the evidence used to update them, including labels, preference data, reward estimates, and observed state transitions. This agenda spans visual recognition, video generation, world models, and visual post-training.

My long-term goal is to build agents that can predict the consequences of candidate actions, execute an action, compare predicted and observed state transitions, and use the discrepancy to improve their models or policies. I study this closed loop in digital environments, where interaction can be repeated and scaled, and in physical environments, where actions must satisfy geometric, dynamical, and safety constraints.

I am always looking for highly motivated research interns and long-term collaborators. We currently have multiple positions available, focusing on, but not limited to, multimodal large models, video generation/editing, and 3D generation. If you are interested in exploring these areas or discussing potential research collaborations, please feel free to contact me via email. (Applicants for internships are encouraged to include your CV.)

Work Experience

  • Jul 2021 - Dec 2021, Alibaba DAMO Academy
    • Research intern working on virtual human synthesis.

  • Jul 2020 - Jul 2021, Baidu Research
    • Research intern working on visual knowledge embedding, object recognition, and multi-modal representation.

  • Mar 2020 - Jun 2020, JD AI Research
    • Research intern working on product recognition.

  • Aug 2018 - Jun 2019, JD AI Research
    • Research intern working on visual-language representation learning.

Selected Honors

  • First place in AliProducts Challenge @ CVPR 2020 the RetailVision workshop.
  • First place in iMat Product Competition @ CVPR 2019 FGVC6 workshop.
  • First place in in Fieldguide Challenge: Moths & Butterflies @ CVPR 2019 FGVC6 workshop.
  • Second place in iFood Competition @ CVPR 2019 FGVC6 workshop.
  • Second place in iMet2020 Fine-grained Attributes Classification Competition @ CVPR 2020 FGVC7 workshop.
  • Kaggle Silver Medal in Deepfake Detection Challenge 2020.

Recent Work (all publications)

Selected Publications

Note *: interns that I mentored. Note †: equal contribution.

Journal Reviewer

  • Reviewer for TPAMI and TIP.

    • Conference Reviewer / Program Committee Member

  • Reviewer for ICCV, CVPR, ICLR, NeurIPS, ECCV, MM, AAAI, IJCAI, ICME, and CAAI.

    • Others

  • Member of MNBVC (Massive Never-ending BT Vast Chinese corpus).
    • Visitor map for akira-l.github.io