Can Qin 秦灿
Ph.D Student
Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA.
Office: Richard Hall, 360 Huntington Ave, Boston, MA 02115
Email : [at]     CV     Github     Google Scholar

About Me

I’m a second-year Ph.D student in the Department of Electrical & Computer Engineering, Northeastern University (NEU) under the supervision of Prof. Yun Raymond Fu. I received my B.E. degree from the School of Microelectronics, Xidian University (XDU), China, in 2018. My research interest lies in machine learning and computer vision.

Research Interests

  • Transfer Learning and Domain Adaptation.
  • Self-supervised/Semi-supervised/Few-shot/Zero-shot Learning.
  • Deep Learning for Image Classification, Segmentation and 3D Vision.


  • 2020.07: Our paper is accepted by ECCV 2020 as Poster.
  • 2020.05: Our paper is accepted by CVPR Workshop on Fair, Data Efficient and Trusted Computer Vision, 2020.
  • 2019.12: I have been invited as a program committee (PC) member for IJCAI-PRICAI 2020 .
  • 2019.11: Our paper is accepted by AAAI 2020 as Poster.
  • 2019.10: Our paper is awarded as the Best Paper of ICCV Workshop on RLQ, 2019.
  • 2019.09: Our paper is accepted by NeurIPS 2019 as Poster.
  • 2019.08: Our paper is accepted by ICCVW on RLQ, 2019 as Oral.
  • 2019.06: Start my internship at Adobe in San Jose.
  • 2018.09: Begin my journey in Smile Lab, Northeastern University at Boston.


Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning
Yunyu Liu, Lichen Wang, Yue Bai, Can Qin, Zhengming Ding, Yun Fu
European Conference on Computer Vision (ECCV), 2020.
[Paper] [Code]
Face Recognition: Too Bias, or Not Too Bias?
Joseph P Robinson, Gennady Livitz, Yann Henon, Can Qin, Yun Fu, Samson Timoner
CVPR Workshop on Fair, Data Efficient and Trusted Computer Vision, 2020.
[Paper] [Code]
Opposite Structure Learning for Semi-supervised Domain Adaptation
Can Qin, Lichen Wang, Qianqian Ma, Yu Yin, Huan Wang, Yun Fu
arXiv preprint arXiv:2002.02545, 2020.
Dual Relation Semi-supervised Multi-label Learning
Lichen Wang, Yunyu Liu, Can Qin, Gan Sun, Yun Fu
AAAI Conference on Artificial Intelligence (AAAI), 2020.
[Paper] [Code]
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
Can Qin*, Haoxuan You*, Lichen Wang, C.-C. Jay Kuo, Yun Fu. (* equal contribution)
Advances in Neural Information Processing Systems (NeurIPS), 2019.
[Paper] [Code]
Generatively Inferential Co-Training for Unsupervised Domain Adaptation
Can Qin, Lichen Wang, Yulun Zhang, Yun Fu.
ICCV Workshop on Real-World Recognition from Low-Quality Images and Videos, 2019. (Best Paper Award )
[Paper] [Code]
Efficient Scene Labeling via Sparse Annotations
Can Qin, Maoguo Gong, Yue Wu, Dayong Tian, Puzhao Zhang.
Smart IoT Workshop at the AAAI Conference on Artificial Intelligence, 2018.
A Multi-objective Framework for Location Recommendation Based on User Preference
Shanfeng Wang, Maoguo Gong, Can Qin, Junwei Yang
IEEE Conference on Computational Intelligence and Security (CIS), 2017
Local Probabilistic Matrix Factorization for Personal Recommendation
Wenping Ma, Yue Wu, Maoguo Gong, Can Qin, Shanfeng Wang.
IEEE Conference on Computational Intelligence and Security (CIS), 2017


  • Best Paper Award of ICCV Workshop on RLQ, 2019
  • The Star of 2018-Graduates in XDU (Highest honor) , 2018
  • The First Prize Scholarship in XDU, 2016, 2017
  • Meritorious Winner of the Interdisciplinary Contest in Modeling, 2016
  • Outstanding Student Leader in XDU, 2015

Professonal Activities

  • Reviewer for IJCAI-PRICAI 2020.
  • External Reviewer for IEEE Computational Intelligence Magazine.
  • Volunteer for 13th IEEE Conference on Automatic Face and Gesture Recognition, 2018.

Programming Skills

  • Language: Python, MATLAB, C, LATEX and others.
  • Machine Learning Frameworks: PyTorch, TensorFlow, Keras, Sklearn, OpenCV and others.