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 : qin.ca [at] husky.neu.edu   qin.ca [at] northeastern.edu     CV     Github     Google Scholar

About Me

I am a third-year Ph.D. student in the Smile Lab of Department of Electrical and 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 interests mainly include the 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.

News

  • 2020.09: I have been invited as a program committee (PC) member for AAAI 2021 .
  • 2020.08: I have been invited as a program committee (PC) member for IJCAI 2021 .
  • 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.

Publications

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.
[Paper]
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.
[Paper]
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
[Paper]
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
[Paper]

Awards

  • 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.