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Currently my research focuses on generative models. More specifically, I'm interested in:

• Multi-modal generative models (assistants) which are more user-friendly;
• Customizing pre-trained generative models;
• Saving the training or dataset construction cost in generative modeling;

I obtained my Ph.D. from the Department of Computer Science and Engineering, University at Buffalo, under the supervision of Prof. Jinhui Xu and Prof. Changyou Chen. I received my B.E. degree from Zhejiang University.

I worked as a Research Intern with Chunyuan Li (Microsoft), Ruiyi Zhang (Adobe), Bingchen Liu (ByteDance).

If you have interest in interning at Adobe, feel free to send me an email with your CV and a short overview of the topics that interest you.

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• Customization Assistant for Text-to-Image Generation

  Yufan Zhou, Ruiyi Zhang, Jiuxiang Gu, Tong Sun.

  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

  An assistant which can generate creative images for specific user-input subject along with text explanation and elaboration in 2-5 seconds, without any fine-tuning.


• Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free Approach

  Yufan Zhou, Ruiyi Zhang, Tong Sun, Jinhui Xu.

  A novel framework for customized text-to-image generation without the use of regularization.
  We can efficiently customize a large-scale text-to-image generation model on single GPU, with only one image provided by the user.


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  Shifted Diffusion for Text-to-image Generation

  Yufan Zhou, Bingchen Liu, Yizhe Zhu, Xiao Yang, Changyou Chen, Jinhui Xu.

  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

  We propose a method termed Corgi, which can better generate image embeddings from text inside multimodal embedding space.
  It benefits both standard and language-free text-to-image generation. And yes, I do have a Corgi.


• LAFITE: Towards Language-Free Training for Text-to-Image Generation

  Yufan Zhou, Ruiyi Zhang, Changyou Chen, Chunyuan Li, Chris Tensmeyer, Tong Yu, Jiuxiang Gu, Jinhui Xu, Tong Sun.

  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

  Our proposed work, Lafite, is the first work which can successfully train text-to-image generation model with image-only dataset.


• TiGAN: Text-Based Interactive Image Generation and Manipulation

  Yufan Zhou, Ruiyi Zhang, Jiuxiang Gu, Chris Tensmeyer, Tong Yu, Changyou Chen, Jinhui Xu, Tong Sun.

  AAAI conference on Artificial Intelligence (AAAI), 2022.


• Meta-Learning with Neural Tangent Kernels

  Yufan Zhou*, Zhenyi Wang*, Jiayi Xian, Changyou Chen, Jinhui Xu.

  International Conference on Learning Representations (ICLR), 2021.


• Learning Manifold Implicitly via Explicit Heat Kernel Learning

  Yufan Zhou, Changyou Chen, Jinhui Xu.

  Conference on Neural Information Processing Systems (NeurIPS), 2020.

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• Conferences Program Committee/Reviewer: NeurIPS 2020, 2021, 2022, 2023; ICML 2021, 2022, 2023, 2024; ICLR 2022, 2023, 2024; CVPR 2023, 2024; AISTATS 2021; AAAI 2021, 2022; IJCAI 2021; EMNLP 2022, 2023; ECCV 2024; ACL 2023;
• Journal Reviewer: IEEE Transactions on Neural Networks and learning systems; IEEE Transactions on Circuits and Systems for Video Technology;