Xiaoyan Zhang

I am a master's student at the University of Michigan, advised by Prof. Qing Qu. My research interest is on multi-agent systems, generative models, and large-scale representation learning. I received a B.E. degree in Artificial Intelligence from Anhui University. In Fall 2024, I collaborated with the Video and Image Processing Laboratory (VIPER), where I worked on continual learning and food recognition.

Email: xyzaxis AT umich.edu

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Xiaoyan Zhang Picture

Education

AHU
Anhui University 2021–2025
Bachelor of Engineering in Artificial Intelligence
GPA: 4.34/5.00, Rank: 1/251

Selected Publications

SIGMA: Selective-Interleaved Generation with Multi-Attribute Tokens
Xiaoyan Zhang, Zechen Bai, Haofan Wang, Yiren Song
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026
Paper / Code /
@article{zhang2026sigma,
  title={SIGMA: Selective-Interleaved Generation with Multi-Attribute Tokens},
  author={Zhang, Xiaoyan and Bai, Zechen and Wang, Haofan and Song, Yiren},
  journal={arXiv preprint arXiv:2602.07564},
  year={2026}
}

SIGMA introduces a selective-interleaved generation framework with multi-attribute tokens, enabling more controllable and compositional visual generation across diverse attributes.
Dual-Imbalance Continual Learning for Real-World Food Recognition
Xiaoyan Zhang, Jiangpeng He
CVPR MetaFood Workshop, 2026
Best Paper Award
Paper / Code /
@article{zhang2026dual,
        title={Dual-Imbalance Continual Learning for Real-World Food Recognition},
        author={Zhang, Xiaoyan and He, Jiangpeng},
        journal={arXiv preprint arXiv:2603.29133},
        year={2026}
      }

This paper studies dual-imbalance continual learning in real-world food recognition, addressing both class imbalance and data imbalance to improve robust incremental adaptation in practical scenarios.
One Adapter for All: Towards Unified Representation in Step-Imbalanced Class-Incremental Learning
Xiaoyan Zhang, Jiangpeng He
arXiv preprint, 2026
Paper / Code /
@article{zhang2026one,
  title={One Adapter for All: Towards Unified Representation in Step-Imbalanced Class-Incremental Learning},
  author={Zhang, Xiaoyan and He, Jiangpeng},
  journal={arXiv preprint arXiv:2603.10237},
  year={2026}
}

One-A presents a unified adapter merging framework for step-imbalanced class-incremental learning, improving representation consistency while maintaining efficient deployment with a single merged adapter.

Teaching Experience

  • Fall 2024: ZX52340 Java Technology and Its Application (Practice), Teaching Assistant Anhui University
  • Fall 2024: ZJ52014 Introduction to Artificial Intelligence, Teaching Assistant Anhui University

Awards

  • Best Paper Award, CVPR 3rd MetaFood Workshop 2026
  • Outstanding Graduate of Anhui Province, Ministry of Education of Anhui Province 2025
  • National Scholarship, Ministry of Education of the People's Republic of China 2024
  • Song Qingling Future Scholarship (0.05%), The China Song Qingling Foundation 2024
  • Excellent Student Scholarship, Anhui University 2023
  • Academic Science and Technology Scholarship, Anhui University 2023