Can Qin

Salesforce AI Research, 181 Lytton Avenue, Palo Alto, CA, 94301, USA

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Email: cqin[at]salesforce.com or qin.ca[at]northeastern.edu

Hello and welcome! I’m currently embracing the exciting world of artificial intelligence as a Research Scientist at Salesforce AI Research. My journey is driven by a deep passion for Generative AI and Multi-modal Learning, with a focus on developing Foundation Models and Autonomous AI Agents that are not only highly generalized but also multifunctional and controllable.

In 2023, I earned my Ph.D. from Northeastern University in Boston, USA. My research during this period was primarily centered around the realms of Transfer Learning and Efficient AI, where I delved into complex problems and innovative solutions.

Before my Ph.D. journey, I obtained my B.E. degree from Xidian University in Xi’an, China, in 2018. This foundation laid the groundwork for my ongoing pursuit of knowledge and innovation.

news

Nov, 2023 Begin my journey at Salesforce Research in Palo Alto!
Jun, 2023 I have passed the PhD Dissertation Defense and become Dr. Qin!

selected publications

  1. arXiv
    HIVE: Harnessing Human Feedback for Instructional Visual Editing
    Shu Zhang, Xinyi Yang, Yihao Feng, Can Qin, Chia-Chih Chen, Ning Yu, Zeyuan Chen, Huan Wang, Silvio Savarese, Stefano Ermon, Caiming Xiong, and Ran Xu
    arXiv preprint arXiv:2303.09618, 2023
    arXiv Code
  2. NeurIPS
    UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild
    Can Qin, Shu Zhang, Ning Yu, Yihao Feng, Xinyi Yang, Yingbo Zhou, Huan Wang, Juan Carlos Niebles, Caiming Xiong, Silvio Savarese, Stefano Ermon, Yun Fu, and Ran Xu
    Advances in Neural Information Processing Systems, 2023
    arXiv Code Website
  3. ICCV
    GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation
    Can Qin, Ning Yu, Chen Xing, Shu Zhang, Zeyuan Chen, Stefano Ermon, Yun Fu, Caiming Xiong, and Ran Xu
    International Conference on Computer Vision, 2023
    arXiv Code Website