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常虹
常虹 研究员
电子邮箱: changhong@ict.ac.cn; hong.chang@vipl.ict.ac.cn
通讯地址: 北京市海淀区科学院南路6号 中国科学院计算技术研究所534室
研究方向: 机器学习,模式识别,计算机视觉
个人简介

常虹,研究员,博士生导师。先后于1998年、2001年和2006年毕业于河北工业大学、天津大学和香港科技大学,获计算机科学本科、硕士和博士学位。曾任施乐欧洲研究中心研究员,2008年加入中科院计算所。主要研究方向包括机器学习与模式识别的方法、模型以及在图像处理、计算机视觉、数据挖掘等方面的应用。

经历

教育经历

2001-2006: The Hong Kong University of Science and Technology, Hong Kong, China

1998-2001: Tianjin University, China

1994-1998: Hebei University of Technology, China

学术经历

2008.3-now: Associate Researcher/ Researcher at Institute of Computing Technology, CAS, Beijing, China

2016.9-2018.6: Visiting professor at KU Leuven, Belgium

2006.11-2007.12: Research Scientist at Xerox Research Centre Europe, France


学术服务

刊物服务

[1]   Associate Editor of: Pattern Recognition

[2]   Associate Editor of: Web Intelligence and Agent Systems

[3]   Reviewer of: TPAMI, TNNLS, TIP, CSVT, TIST, PR, etc.

会议服务

[1]   Program committee member of: IJCAI'2015/2018, UAI'2015/2017, ICCV'2015/2017, CVPR'2014/2015/2016/2017/2019, ECCV'2014/2018, AAAI'2014/2016/2017/2018/2019, etc.

[2]   Local team member, ICML'2014

[3]   Speaker coordinator, CNCC'2012

研究内容

1.   Algorithms and models for machine learning and pattern recognition

particularly metric learning, deep neural networks, manifold learning, kernel methods etc.

2.   Applications of machine learning methods on CV and PR problems

particularly image/video representation, object detection, tracking and recognition, etc.


著论

论文

Journal: (selected)  

[1] R. Hou, B. Ma, H. Chang, X. Gu, S. Shan, X. Chen. IAUnet: global context-aware feature learning for person re-identification. IEEE Trans. on Neural Networks and Learning Systems. To appear. 

[2] F. Xu, B. Ma, H. Chang, S. Shan. Isosceles constraints for person re-identification. IEEE Trans. on Image Processing, vol. 29, no. 11, pp. 8930-8943, Nov. 2020. 

[3] K. Liang, H. Chang,, S. Shan and X. Chen. Unifying Visual Attribute Learning with Object Recognition in a Multiplicative Framework. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 41, no. 7, pp. 1747-1760, July 2019. 

[4] W. Zheng, H. Chang, L. Liang, H. Ren, S. Shan, and X. Chen . Strip features for fast object detection. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 43, no. 6, 1898-1912, December, 2013. 

[5] D. Zhai, H. Chang, S. Shan, X. Chen, W. Gao. Multi-View Metric Learning with Global Consistency and Local Smoothness. ACM Transactions on Intelligent Systems and Technology. Volume 3 Issue 3, May 2012.


Conference: (most recent)  

[1] R. Hou, H. Chang, B. Ma, S. Shan, and X. Chen. Temporal Complementary Learning for Video Person Re-Identification. ECCV, 2020. 

[2] X. Gu, H. Chang, B. Ma, H. Zhang, and X. Chen. Appearance-Preserving 3D Convolution for Video-based Person Re-identification. ECCV, 2020. 

[3] H. Zhang, H. Chang, B. Ma, N. Wang, X. Chen. Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training. ECCV, 2020. 

[4] C. Wang, B. Ma, H. Chang, S. Shan and X. Chen. TCTS: A Task-Consistent Two-stage Framework for Person Search. CVPR, 2020. 

[5] R. Hou, H. Chang, B. Ma, S. Shan and X. Chen. Cross Attention Network for Few-shot Classification. NeurIPS, 2019. 

[6] X. Gu, B. Ma, H. Chang, S. Shan and X. Chen. Temporal Knowledge Propagation for Image-to-Video Person Re-identification. ICCV, 2019. 

[7] R. Hou, B. Ma, H. Chang, X. Gu, S. Shan and X. Chen. VRSTC: Occlusion-Free Video Person Re-Identification. CVPR, 2019. 

[8] R. Hou, B. Ma, H. Chang, X. Gu, S. Shan and X. Chen. Interaction-and-Aggregation Network for Person Re-identification. CVPR, 2019. 

[9] S. Li, B. Ma, H. Chang, S. Shan and X. Chen. Continuity-Discrimination Convolutional Neural Network for Visual Object Tracking. ICME, 2018. (Platinum Best Paper Award)