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Qi QianPh.D.Email: qianq.mail [at] gmail [dot] com OR qi.qian [at] zoom [dot] us |
I joined Zoom as a research scientist in 2024. Before that, I worked for the research lab of Alibaba at Seattle from 2015 to 2024. I obtained the Ph.D. degree in Computer Science from Department of Computer Science and Engineering, Michigan State University in August 2015, under the supervision of Dr. Rong Jin .
I received my B.Sc. and M.Sc. degrees in Computer Science from Nanjing University in June 2008 and June 2011, respectively. As a master student, I joined the LAMDA group and my supervisor was Dr. Zhi-Hua Zhou.
Machine Learning: Distance Metric Learning, Multiple Clustering, Online Learning
Computer Vision: Fine-grained Visual Categorization, Representation Learning, Object Detection
Multi-modal Learning: Multi-modal Representation Learning
| Q. Qian, J. Hu. Online Zero-Shot Classification with CLIP. In: Proceedings of the 18th European Conference on Computer Vision (ECCV'24), Milan, Italy, 2024. [Pre-Print PDF] [Code] | |
| Q. Qian, Y. Xu, J. Hu. SeA: Semantic Adversarial Augmentation for Last Layer Features from Unsupervised Representation Learning. In: Proceedings of the 18th European Conference on Computer Vision (ECCV'24), Milan, Italy, 2024. [Pre-Print PDF] [Code] | |
| J. Yao, Q. Qian, J. Hu. Multi-Modal Proxy Learning Towards Personalized Visual Multiple Clustering. In: Proceedings of the 37th IEEE Conference on Computer Vision and Pattern Recognition (CVPR'24), Seattle, WA, 2024. [Pre-Print PDF] [Code] | |
| Q. Qian, Y. Xu, J. Hu. Intra-Modal Proxy Learning for Zero-Shot Visual Categorization with CLIP. In: Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS'23), New Orleans, LA, 2023. [Pre-Print PDF] [Code] | |
| Q. Qian. Stable Cluster Discrimination for Deep Clustering. In: Proceedings of the International Conference on Computer Vision (ICCV'23), Paris, France, 2023. [Pre-Print PDF] [Code] | |
| J. Wang, Y. Xu, J. Hu, M. Yan, J. Sang, Q. Qian. Improved Visual Fine-tuning with Natural Language Supervision. In: Proceedings of the International Conference on Computer Vision (ICCV'23), Paris, France, 2023. [Oral] [Pre-Print PDF] [Code] | |
| Z. Liu, Y. Xu, Y. Xu, Q. Qian, H. Li, X. Ji, A. Chan, R. Jin. Improved Fine-Tuning by Better Leveraging Pre-Training Data. In: Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS'22), New Orleans, LA, 2022. [Pre-Print PDF] [Code] | |
| Q. Qian, Y. Xu, J. Hu, H. Li, R. Jin. Unsupervised Visual Representation Learning by Online Constrained K-Means. In: Proceedings of the 35th IEEE Conference on Computer Vision and Pattern Recognition (CVPR'22), New Orleans, LA, 2022. [Pre-Print PDF] [Code] | |
| Q. Qian, H. Li, J. Hu. Improved Knowledge Distillation via Full Kernel Matrix Transfer. In: Proceedings of the SIAM International Conference on Data Mining (SDM'22), Alexandria, VA, 2022. [Pre-Print PDF] [Code] | |
| Y. Xu, Q. Qian, J. Hu, H. Li, R. Jin. Weakly Supervised Representation Learning with Coarse Labels. In: Proceedings of the International Conference on Computer Vision (ICCV'21), Virtual, 2021. [Pre-Print PDF] [Code] | |
| Y. Xu, L. Shang, J. Ye, Q. Qian, Y. Li, B. Sun, H. Li, R. Jin. Dash: Semi-Supervised Learning with Dynamic Thresholding. In: Proceedings of the 38th International Conference on Machine Learning (ICML'21), Virtual, 2021. [Pre-Print PDF] [Code] | |
| Q. Qian, L. Chen, H. Li, R. Jin. DR Loss: Improving Object Detection by Distributional Ranking. In: Proceedings of the 33rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR'20), Seattle, WA, 2020. [Pre-Print PDF] [Code] | |
| Q. Qian, J. Hu, H. Li. Hierarchically Robust Representation Learning. In: Proceedings of the 33rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR'20), Seattle, WA, 2020. [Pre-Print PDF] [Concepts in ImageNet] | |
| Q. Qian, L. Shang, B. Sun, J. Hu, H. Li, R. Jin. SoftTriple Loss: Deep Metric Learning Without Triplet Sampling. In: Proceedings of the International Conference on Computer Vision (ICCV'19), Seoul, Korea, 2019. [PDF] [Code] | |
| Q. Qian, S. Zhu, J. Tang, R. Jin, B. Sun, H. Li. Robust Optimization over Multiple Domains. In: Proceedings of the 33rd AAAI Conference on Artifical Intelligence (AAAI'19), Honolulu, HI, 2019. [Pre-Print PDF] | |
| Q. Qian, J. Tang, H. Li, S. Zhu and R. Jin. Large-scale Distance Metric Learning with Uncertainty. In: Proceedings of the 31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR'18), Salt Lake City, UT, 2018. [Spotlight] [PDF] | |
| G. Liu, Q. Qian, Z. Wang, Q. Zhao, T. Wang, H. Li, J. Xue, S. Zhu, R. Jin and T. Zhao. The Opensesame NIST 2016 Speaker Recognition Evaluation System. In: Proceedings of the INTERSPEECH (INTERSPEECH'17), Stockholm, Sweden, 2017. | |
| J. Hu, Q. Qian, J. Pei, R. Jin and S. Zhu. Finding Multiple Stable Clusterings. In: Proceedings of the 15th IEEE International Conference on Data Mining (ICDM'15), Atlantic City, NJ, 2015. [Invited to KAIS SI on "Bests of ICDM 2015"] | |
| Q. Qian, R. Jin, S. Zhu and Y. Lin. Fine-Grained Visual Categorization via Multi-stage Metric Learning. In: Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR'15), Boston, MA, 2015. [PDF] [Code] | |
| Q. Qian, J. Hu, R. Jin, J. Pei and S. Zhu. Distance Metric Learning using Dropout: A Structured Regularization Approach. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'14), New York, NY, 2014. [PDF] | |
| J. Yi, L. Zhang, R. Jin, Q. Qian and A. K. Jain. Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion. In: Proceedings of the 30th International Conference on Machine Learning (ICML'13), Atlanta, GA, 2013. [PDF] |
| J. Hu, Q. Qian, J. Pei, R. Jin and S. Zhu. Finding Multiple Stable Clusterings. Knowledge and Information Systems (KAIS), 2016. DOI:10.1007/s10115-016-0998-9. ["Bests of ICDM 2015"] | |
| Q. Qian, R. Jin, J. Yi, L. Zhang and S. Zhu. Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch Stochastic Gradient Descent (SGD). Machine Learning Journal (MLJ), 2015. |
Journal Reviewer: TPAMI, TKDD, TIP, TMM, TBD, EAAI, NEUCOM
Area Chair/Senior PC: ICLR25, IJCAI21
PC Member/Conference Reviewer: CVPR(20, 21, 22, 23, 24), ICCV(19, 21, 23), ECCV(22, 24), ICML(18, 21, 22, 23, 24), NeurIPS(18, 19, 20, 22, 23, 24), ICLR(19, 20), KDD(22, 23, 24), IJCAI(19, 20, 22, 23, 24), AAAI(19, 20, 21)
| Last modifed on Nov 7, 2024. |