專家信息：

周杰 教授 清華大學(xué)自動(dòng)化系，教授，博士生導(dǎo)師。1968年生；1990年畢業(yè)于南開大學(xué)首屆數(shù)學(xué)試點(diǎn)班，獲理學(xué)學(xué)士學(xué)位；1992年提前畢業(yè)于南開大學(xué)數(shù)學(xué)系概率論與數(shù)理統(tǒng)計(jì)專業(yè)，獲理學(xué)碩士學(xué)位；1995年畢業(yè)于華中理工大學(xué)圖像識(shí)別研究所模式識(shí)別與智能控制專業(yè)，獲工學(xué)博士學(xué)位，論文被評(píng)為華中理工大學(xué)優(yōu)秀博士論文；1995年至1997年在清華大學(xué)自動(dòng)化系從事博士后研究；1997年后至今一直在清華大學(xué)自動(dòng)化系工作。2003年提升為教授，2004年提升為博士生導(dǎo)師�，F(xiàn)任清華大學(xué)研究生院院長，副教務(wù)長，曾任清華大學(xué)自動(dòng)化系黨委副書記，現(xiàn)代服務(wù)科學(xué)與技術(shù)研究中心主任。

教育背景：

1986年9月至1990年6月，南開大學(xué)數(shù)學(xué)試點(diǎn)班學(xué)習(xí)，獲理學(xué)士學(xué)位。

1990年9月至1992年6月，南開大學(xué)數(shù)學(xué)系概率論與數(shù)理統(tǒng)計(jì)專業(yè)學(xué)習(xí)，獲理學(xué)碩士學(xué)位。

1992年9月至1995年9月，華中理工大學(xué)圖象識(shí)別研究所模式識(shí)別與智能控制專業(yè)學(xué)習(xí)，獲工學(xué)博士學(xué)位。

工作經(jīng)歷;

1995年11月至1997年10月，清華大學(xué)自動(dòng)化系 博士后

1997年11月至1998年7月，清華大學(xué)自動(dòng)化系 講師

1998年8月至2003年11月，清華大學(xué)自動(dòng)化系 副教授

2003年12月至今，清華大學(xué)自動(dòng)化系 教授

2004年8月至今，清華大學(xué)自動(dòng)化系 博士生導(dǎo)師

學(xué)術(shù)兼職：

1、2016年12月至今 IAPR Fellow。

2、2004年11月至今 IEEE Senior Member。

3、2004年1月至今 國際刊物《Int. Journal of Robotics & Automation》 （SCI）編委。

4、2006年5月至2014年12月 《自動(dòng)化學(xué)報(bào)》編委。

5、2016年12月至今《模式識(shí)別與人工智能》副主編。

6、2013年12月至今 中國自動(dòng)化學(xué)會(huì)教育工作委員會(huì)主任委員。

7、2014年12月至今 中國人工智能學(xué)會(huì)模式識(shí)別專委會(huì)副主任委員。

8、2014年4月至今 模式識(shí)別國家重點(diǎn)實(shí)驗(yàn)室學(xué)術(shù)委員會(huì)委員。

9、《IEEE Transaction on Pattern Analysis and Machine Intelligence》、《IEEE Transaction on Image Processing》、《Pattern Recognition》、《中國科學(xué)》等重要刊物審稿人。

10、ICCV 2003、2005 Programming Committee Member。

11、“全國自動(dòng)化系主任（院長）論壇”秘書長。

12、“2006年亞太地區(qū)服務(wù)科學(xué)、管理與工程研討會(huì)”執(zhí)行主席。

13、清華大學(xué)自動(dòng)控制學(xué)科分學(xué)位委員會(huì)委員。

主講課程：

數(shù)值分析, 本科生

系統(tǒng)與控制理論中的線性代數(shù), 研究生

科學(xué)研究：

研究領(lǐng)域：

[1] 模式識(shí)別

指紋識(shí)別，掌紋識(shí)別，人臉識(shí)別，機(jī)器學(xué)習(xí)

[2] 計(jì)算機(jī)視覺

視覺監(jiān)控，仿生雙目視覺，醫(yī)學(xué)圖像處理，圖像分析與檢索

承擔(dān)科研項(xiàng)目情況：

近年來主持國家自然科學(xué)基金項(xiàng)目、北京市自然科學(xué)基金項(xiàng)目、博士點(diǎn)基金項(xiàng)目、國際合作項(xiàng)目及重大橫向項(xiàng)目共10多項(xiàng)，總經(jīng)費(fèi)超過500萬元。

[1] 國家杰出青年科學(xué)基金項(xiàng)目：模式識(shí)別與視覺監(jiān)控，2013年1月至2016年12月。

[2] 國家自然科學(xué)基金重大國際(地區(qū))合作研究項(xiàng)目：生物特征識(shí)別中的新方法及其應(yīng)用，2011年1月至2014年12月。

[3]國家自然科學(xué)基金項(xiàng)目：“音樂自動(dòng)分析與識(shí)別研究”。

[4]教育部博士點(diǎn)基金：“復(fù)雜背景下的行人檢測方法研究”。

[5]交通部科研項(xiàng)目：“基于視頻分析的水路航運(yùn)信息分析系統(tǒng)”。

[6]國際合作項(xiàng)目：“信息服務(wù)學(xué)科建設(shè)和相關(guān)技術(shù)研究”。

[7]企業(yè)合作項(xiàng)目：“視覺監(jiān)控應(yīng)用研究”。

[8]國際合作項(xiàng)目：“單目鏡頭下行人檢測方法研究”。

[9]國家自然科學(xué)基金重點(diǎn)項(xiàng)目：“基于生物特征的身份識(shí)別研究”。

[10]國家自然科學(xué)基金資助：“指紋自動(dòng)識(shí)別新方法研究”。

[11]北京市自然科學(xué)基金資助：“新一代指紋識(shí)別方法研究”。

[12]國際合作項(xiàng)目：“老年指紋識(shí)別研究”。

[13]企業(yè)合作項(xiàng)目：“視頻交通信息處理系統(tǒng)的研制與開發(fā)”。

[14]清華大學(xué)重點(diǎn)攻關(guān)項(xiàng)目：“基于生物統(tǒng)計(jì)學(xué)的人身鑒定方法”。

[15]國家自然科學(xué)基金資助：“基于模擬畫像原理的人臉識(shí)別方法研究”。

發(fā)明專利：

1、“用于音樂試聽的流行歌曲關(guān)鍵段提取方法”，專利號(hào)：200410009537.0，發(fā)明人：周杰，張一彬，馮上平等，公告日：2005.03.23

2、“基于斷紋檢測的指紋識(shí)別方法”，專利號(hào)：200410062256.1，發(fā)明人：周杰，吳南南，楊春宇，公告日：2005.03.02

3、“指紋多特征識(shí)別方法”，專利號(hào)：200410009328.6，發(fā)明人：周杰，顧金偉，萬定銳，公告日：2005.03.16

4、“多注冊指紋融合方法”，專利號(hào)：200410068976.9，發(fā)明人：周杰，楊春宇，公告日：2005.03.02

5、“基于密度圖模型的指紋識(shí)別方法”，專利號(hào)：200410068977.3，發(fā)明人：周杰，萬定銳，公告日：2005.03.16

論文專著：

發(fā)表英文論文：

近年來在國內(nèi)外學(xué)術(shù)刊物及國際會(huì)議上發(fā)表論文50多篇。其中在國際期刊（包括IEEE Transaction on Pattern Analysis and Machine Intelligence、IEEE Trans on Image Processing、Pattern Recognition等本專業(yè)權(quán)威期刊）上發(fā)表論文14篇（其中影響因子超過2的8篇），SCI索引18篇、SCI他引53次。在國際CVPR大會(huì)上發(fā)表6篇論文。相關(guān)研究成果受到國際同行的高度評(píng)價(jià)。

1. Zhixiang Chen, Jiwen Lu, Jianjiang Feng, and Jie Zhou. Nonlinear Structural Hashing for Scalable Video Search. IEEE Transactions on Circuits and Systems for Video Technology, to appear

3. Zhixiang Chen, Jiwen Lu, Jianjiang Feng, and Jie Zhou. Nonlinear Discrete Hashing. IEEE Trans. on Multimedia, to appear

4. Venice Erin Liong, Jiwen Lu, Yap-Pen Tan, and Jie Zhou. Deep Coupled Metric Learning for Cross-modal Matching. IEEE Trans. on Multimedia, to appear

5. Hao Liu, Jiwen Lu, Jianjiang Feng, and Jie Zhou. Group-Aware Deep Feature Learning for Facial Age Estimation. Pattern Recognition, to appear

6. Xuanbin Si, Jianjiang Feng, Bo Yuan, and Jie Zhou. Dense Registration of Fingerprints. Pattern Recognition, to appear

7. Yueqi Duan, Lei Chen, Jiwen Lu, and Jie Zhou, Deep embedding learning with discriminative sampling policy, IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Bench, June, 2019.

8. Wenzhao Zheng, Jiwen Lu, Zhaodong Chen, and Jie Zhou, Hardness-aware deep metric learning, IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Bench, June, 2019.

9. Ziwei Wang, Jiwen Lu, Chenxi Tao, and Jie Zhou, Learning channel-wise interactions for binary convolutional neural networks, IEEE/CVF Computer Society Conference on Computer Vision and Pattern Recognition, Long Bench, June, 2019.

10. Xin Yuan, Liangliang Ren, Jiwen Lu, and Jie Zhou, Enhanced Bayesian compression via deep reinforcement learning, IEEE/CVF Computer Society Conference on Computer Vision and Pattern Recognition, Long Bench, June, 2019.

11. Yueqi Duan, Lei Chen, Jiwen Lu, and Jie Zhou, Deep embedding learning with discriminative sampling policy, IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Bench, June, 2019.

12.Hao Liu, Jiwen Lu*, Minghao Guo, Suping Wu, and Jie Zhou, Learning Reasoning-Decision Networks for Robust Face Alignment, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.

13.Chunze Lin, Jiwen Lu*, and Jie Zhou, Multi-grained Deep Feature Learning for Robust Pedestrian Detection, IEEE Transactions on Circuits and Systems for Video Technology, 2019.

14.Lijie Liu, Jiwen Lu*, Chunjing Xu, Qi Tian, Jie Zhou, Deep Relational Reasoning Network for Monocular 3D Object Detection, CVPR, 2019.

15.Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu*, Jie Zhou, COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis, CVPR, 2019.

16.Yueqi Duan, Yu Zheng, Jiwen Lu*, Jie Zhou, and Qi Tian, Structural Relational Reasoning of Point Clouds, CVPR, 2019.

17.Yongming Rao, Jiwen Lu*, and Jie Zhou, Spherical Fractal Convolution Neural Networks for Point Cloud Recognition, CVPR, 2019.

18.Yueqi Duan, Jiwen Lu, and Jie Zhou, UniformFace: Learning deep Equidistributed representation for face recognition, IEEE International Conference on Computer Vision and Pattern Recognition, 2019.

19.Wanhua Li, Jiwen Lu, Jianjiang Feng, Chunjing Xu, Jie Zhou, and Qi Tian, BridgeNet: a continuity-aware Probabilistic Network for Age Estimation, IEEE International Conference on Computer Vision and Pattern Recognition, 2019.

20.Guangyi Chen, Jiwen Lu*, Ming Yang, and Jie Zhou, Spatial-Temporal Attention-aware Learning for Video-based Person Re-identification, IEEE Transactions on Image Processing, 2019.

21. Yueqi Duan, Jiwen Lu, Ziwei Wang, Jianjiang Feng, and Jie Zhou, Learning deep binary descriptor with multi-quantization, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, accepted.  

22. Kevin Lin, Jiwen Lu, Chu-Song Chen, Jie Zhou, and Ming-Ting Sun, Unsupervised deep learning of compact binary descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, accepted.

23. Venice Erin Liong, Jiwen Lu, Lin-Yu Duan, and Yap-Peng Tan, Deep variational and structural hashing, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, accepted.

24. Jiwen Lu, Venice Erin Liong, and Jie Zhou, Simultaneous local binary feature learning and encoding for homogeneous and heterogeneous face recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 8, pp. 1979-1993, 2018.

25. Yueqi Duan, Jiwen Lu, Jianjiang Feng, and Jie Zhou, Context-aware local binary feature learning for face recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, pp. 1139-1153, 2018.

26. Yueqi Duan, Ziwei Wang, Jiwen Lu*, Xudong Lin, and Jie Zhou, GraphBit: bitwise interaction mining via deep reinforcement learning, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake, June, 2018. 

27. Xin Yuan, Liangliang Ren, Jiwen Lu*, and Jie Zhou, Relaxation-free deep hashing via policy gradient, European Conference on Computer Vision, Munich, Sep., 2018.

28. Zhixiang Chen, Xin Yuan, Jiwen Lu*, Qi Tian, and Jie Zhou, Deep hashing by discrepancy minimization, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake, June, 2018.

29. Hao Liu, Jiwen Lu, Jianjiang Feng, and Jie Zhou, Two-stream transformer networks for video-based face alignment, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 11, pp. 2546-2554, 2018.

30. Yongming Rao, Jiwen Lu, and Jie Zhou, Learning discriminative aggregation network for video-based face recognition and person re-identification, International Journal of Computer Vision, 2018, accepted.

31. Xudong Lin, Yueqi Duan, Qiyuan Dong, Jiwen Lu, and Jie Zhou, Deep variational metric learning, European Conference on Computer Vision, Munich, Sep., 2018.

32. Yueqi Duan, Wenzhao Zheng, Xudong Lin, Jiwen Lu, and Jie Zhou, Deep adversarial metric learning, IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake, June, 2018.

33. Yongming Rao, Jiwen Lu, Ji Lin, and Jie Zhou, Runtime network routing for efficient image classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, accepted.

34. Hao Liu, Jiwen Lu, Minghao Guo, Suping Wu, and Jie Zhou, Learning reasoning-decision networks for robust face alignment, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, accepted.

35. Minghao Guo, Jiwen Lu, and Jie Zhou, Dual-agent deep reinforcement learning for deformable face tracking, European Conference on Computer Vision, Munich, Sep., 2018.

36. Lei Chen, Jiwen Lu, Zhanjie Song, and Jie Zhou, Part-activated deep reinforcement learning for action prediction, European Conference on Computer Vision, Munich, Sep., 2018.  

37. Liangliang Ren, Xin Yuan, Jiwen Lu, Ming Yang, and Jie Zhou, Deep reinforcement learning with iterative shift for visual tracking, European Conference on Computer Vision, Munich, Sep., 2018.

38. Liangliang Ren, Jiwen Lu, Zifeng Wang, Qi Tian, and Jie Zhou, Collaborative deep reinforcement learning for multi-object tracking, European Conference on Computer Vision, Munich, Sep., 2018.

39. Yansong Tang, Yi Tian, Jiwen Lu, Peiyang Li, and Jie Zhou, Deep progressive reinforcement learning for skeleton-based action recognition, IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake, June, 2018.

40. Yongming Rao, Dahua Lin, Jiwen Lu, and Jie Zhou, Learning globally optimized object detector via policy gradient, IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake, June, 2018.

41.Chunze Lin, Jiwen Lu*, Gang Wang, and Jie Zhou, Graininess-Aware Deep Feature Learning for Pedestrian Detection, ECCV, 2018. 

42. Jiwen Lu, Venice Erin Liong, and Jie Zhou, Deep hashing for scalable image search, IEEE Transactions on Image Processing, vol. 26, no. 5, pp. 2352-2367, 2017.

43. Yueqi Duan, Jiwen Lu, Jianjiang Feng, and Jie Zhou, Learning rotation-invariant local binary descriptor, IEEE Transactions on Image Processing, vol. 26, no. 8, pp. 3636-3651, 2017.

44. Venice Erin Liong, Jiwen Lu*, Yap-Peng Tan, and Jie Zhou, Cross-modal deep variational hashing, IEEE International Conference on Computer Vision, Venice, Oct., 2017.

45. Yueqi Duan, Jiwen Lu*, Ziwei Wang, Jianjiang Feng, and Jie Zhou, Learning deep binary descriptor with multi-quantization, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Honolulu, July, 2017.

46. Jiwen Lu, Junlin Hu, and Jie Zhou, Deep metric learning for visual understanding: an overview of recent advances, IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 76-84, 2017.

47. Yongming Rao, Ji Lin, Jiwen Lu, and Jie Zhou, Learning discriminative aggregation networks for video-based face recognition, IEEE/CVF International Conference on Computer Vision, Venice, Oct., 2017.

48. Ji Lin, Liangliang Ren, Jiwen Lu, Jianjiang Feng, and Jie Zhou, Consistent-aware deep learning for person re-identification in a camera network, IEEE/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, July, 2017. 

49.Ji Lin, Yongming Rao, Jiwen Lu, and Jie Zhou, Runtime neural pruning, Advances in Neural Information Processing Systems, Long Bench, Dec., 2017.

50. Yongming Rao, Jiwen Lu, and Jie Zhou, Attention-aware deep reinforcement learning for video based face recognition, IEEE International Conference on Computer Vision, Venice, Oct., 2017.

51. Hao Liu, Jiwen Lu, Jianjiang Feng, and Jie Zhou. Learning Deep Sharable and Structural Detectors for Face Alignment Group-Aware Deep Feature Learning for Facial Age Estimation. IEEE Transactions on Image Processing, vol. 26, no. 4, pp. 1666-1678, 2017.

52.Jiwen Lu, Gang Wang, and Jie Zhou, Simultaneous feature and dictionary learning for image set based face recognition, IEEE Transactions on Image Processing, vol. 26, no. 8, pp. 4042-4054, 2017.

53. Kevin Lin, Jiwen Lu*, Chu-Song Chen, and Jie Zhou, Learning compact binary descriptors with unsupervised deep neural networks, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, Jun., 2016.

54. Junlin Hu, Jiwen Lu, Yap-Pen Tan, and Jie Zhou. Deep Transfer Metric Learning. IEEE Trans. on Image Processing, vol.25(12), pp.5576-5588, 2016.

55. Renliang Wen, Jiwen Lu, Yap-Pen Tan, and Jie Zhou. Learning Cascaded Deep Auto-encoder Networks for Face Alignment. IEEE Trans. on Multimedia, vol.18(10), pp.2066-2078, 2016.

56. Kailin Ge, Han Hu, Jianjiang Feng, and Jie Zhou. Depth Estimation using a Sliding Camera. IEEE Trans. on Image Processing, vol.25(2), pp.726-739, 2016.

57. Zhenhua Guo, Xingzheng Wang, Jie Zhou, and Jane You. Robust Texture Image Representation by Scale Selective Local Binary Patterns (SSLBP). IEEE Trans. on Image Processing, vol.25(2), pp.687-699, 2016.

58. Yijing Su, Jianjiang Feng, and Jie Zhou. Fingerprint Indexing with Pose Constraint. Pattern Recognition, DOI: 10.1016/j.patcog.2016.01.006

59. Jiwen Lu, Venice Erin Liong, Xiuzhuang Zhou, and Jie Zhou, Learning compact binary face descriptor for face recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 10, pp. 2041-2056, 2015.

60. Jiwen Lu, Venice Erin Liong, and Jie Zhou, Cost-sensitive local binary feature learning for facial age estimation, IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5356-5368, 2015. 

61. Jiwen Lu, Venice Erin Liong, and Jie Zhou, Simultaneous local binary feature learning and encoding for face recognition, IEEE International Conference on Computer Vision, Santiago, Dec., 2015.

62. Venice Erin Liong, Jiwen Lu, Gang Wang, Pierre Moulin, and Jie Zhou, Deep hashing for compact binary codes learning, IEEE Conference on Computer Vision and Pattern Recognition, Boston, Jun.,   (CVPR 2015) , pp.2475-2483

63. Xuanbin Si, Jianjiang Feng, and Jie Zhou, Yuxuan Luo. Detection and Rectification of Distorted Fingerprints. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.37(3), pp.555-568, 2015

64. Han Hu, Jianjiang Feng, and Jie Zhou. Exploiting Unsupervised and Supervised Constraints for Subspace Clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.37(8), pp.1542-1557, 2015

65. Jiwen Lu, Gang Wang, Weihong Deng, Pierre Moulin, and Jie Zhou, Multi-manifold deep metric learning for image set classification, IEEE Conference on Computer Vision and Pattern Recognition, Boston, Jun.,(CVPR 2015), pp.1137-1145

66.Lin Ma, Jiwen Lu, Jianjiang Feng, and Jie Zhou, Multiple Feature Fusion via Weighted Entropy for Visual Tracking, 2015 IEEE International Conference on Computer Vision (ICCV 2015)

67.Lin Ma, Xiaoqin Zhang, Weiming Hu, Junliang Xing, Jiwen Lu, and Jie Zhou, Local Subspace Collaborative Tracking, 2015 IEEE International Conference on Computer Vision (ICCV 2015)

68.Feng, Jianjiang, Jie Zhou, and Yijing SU. "METHOD AND SYSTEM FOR ESTIMATING FINGERPRINT POSE." U.S. Patent No. 20,150,347,804. 3 Dec. 2015.

69.Yang, Xiao,,Jie Zhou et al. "Detection and segmentation of latent fingerprints." Information Forensics and Security (WIFS), 2015 IEEE International Workshop on. IEEE, 2015.

70. Peng Sun, Mark D. Reid, and Jie Zhou. “An Improved Multiclass LogitBoost Using Adaptive-One-vs-One". Machine Learning, 2014 DOI: 10.1007/s10994-014-5434-3

71.Yang, Xiao, Jianjiang Feng, and Jie Zhou. "Localized dictionaries based orientation field estimation for latent fingerprints." Pattern Analysis and Machine Intelligence, IEEE Transactions on 36.5 (2014): 955-969.

72.Luo, Yuxuan, Jianjiang Feng, and Jie Zhou. "Fingerprint matching based on global minutia cylinder code." Biometrics (IJCB), 2014 IEEE International Joint Conference on. IEEE, 2014.

73. Han Hu, Zhouchen Lin, Jianjiang Feng, and Jie Zhou. Smooth Representation Clustering. CVPR 2014, pp.3834-3841 (Oral).

74. Peng Sun, Tong Zhang, and Jie Zhou. "A Convergence Rate Analysis for LogitBoost, MART and Their Variant", in Proceedings of the 31th International Conference on Machine Learning (ICML), 2014

75. Xiangyu Tang, and Jie Zhou. Dynamic Personalized Recommendation using Sparse Data. IEEE Trans. on Knowledge and Data Engineering, vol. 25, no. 12, pp. 2895–2899, 2013.

76. Fanglin Chen, Xiaolin Huang, and Jie Zhou. Hierarchical Minutiae Matching for Fingerprint and Palmprint Identification. IEEE Trans. on Image Processing, vol. 22, no. 11, pp. 4964–4971, 2013

77. Jifeng Dai, Yingnian Wu, Jie Zhou, Songchun Zhu. Cosegmentation and Cosketch by Unsupervised Learning. in ICCV 2013.

78. Peng Sun, and Jie Zhou. "Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner", in Proceedings of the 30th International Conference on Machine Learning (ICML), Atlanta, USA, 2013

79. Peng Sun, Mark Reid, and Jie Zhou. "AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem", in Proceedings of the 29th International Conference on Machine Learning, pp. 1087-1094, Edinburgh, Scotland, 2012

80. Zhihao Zhang, and Jie Zhou. Multi-task clustering via domain adaptation. Pattern Recognition, Vol.45(1), pp.465-473, 2012

81. Heng Su, Liang Tang, Ying Wu, Daniel Tretter, and Jie Zhou. Spatially Adaptive Block-based Super-resolution. IEEE Trans. On Image Processing, Vol.21(3), pp.1031-1045, 2012

82. Heng Su, Ying Wu, and Jie Zhou. Super-resolution without Dense Flow. Accepted as regular paper by IEEE Trans. On Image Processing, Vol.21(4), pp.1782-1795, 2012

83.Dai, Jifeng, Jianjiang Feng, and Jie Zhou. "Robust and efficient ridge-based palmprint matching." IEEE transactions on pattern analysis and machine intelligence 34.8 (2012): 1618-1632.

84.Yang, Xiao, Jianjiang Feng, and Jie Zhou. "Palmprint indexing based on ridge features." 2011 International Joint Conference on Biometrics (IJCB). IEEE, 2011.

85. Jifeng Dai, and Jie Zhou. Multi-feature based high-resolution palmprint recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 33(5), pp.945-957, 2011.

86. Fanglin Chen, Jianjiang Feng, Anil K. Jain, Jie Zhou, and Jin Zhang. Separating Overlapped Fingerprints. IEEE Trans. on Information Forensic and Security, Vol.6(2), pp.346-359, 2011

87. Jie Zhou, Han Hu and Dingrui Wan. Video stabilization and completion using two cameras. IEEE Transactions on Circuits and Systems for Video Technology, Vol.21(12), 2011, pp. 1879-1889.

88. Jie Zhou, Dingrui Wan and Ying Wu. Chameleon-like binocular vision system for visual surveillance. IEEE Signal Processing Magazine, Vol.27(5), pp.91-101, 2010.

89. Chunyu Yang, Yong Cao, Zaiqing Nie, Jie Zhou, and Jirong Wen. Closing the Loop in Webpage Understanding. IEEE Trans. on Knowledge and Data Engineering, Vol.22(5), pp. 639-650, 2010

90. Zhihao Zhang, and Jie Zhou. Transfer estimation of evolving class priors in data stream classification. Pattern Recognition, Vol.43(9), pp.3151-3161, 2010.

91. Han Hu and Jie Zhou. Trajectory Matching from unsynchronized videos. IEEE Computer Vision and Pattern Recognition (CVPR), San Francisco, USA, June 2010.

92. Quanquan Gu, Jie Zhou, Local Learning Regularized Nonnegative Matrix Factorization, The 21st International Joint Conference on Artificial Intelligence (IJCAI), Pasadena, California, USA, 2009, pp.1046-1051.

93. Jie Zhou, Fanglin Chen, and Jinwei Gu, A Novel Algorithm for Detecting Singular Points from Fingerprint Images, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 31(7), pp.1239-1251, July, 2009.

94. Fanglin Chen, Jie Zhou, and Chunyu Yang, Reconstructing orientation field from fingerprint minutiae to improve minutiae matching accuracy, IEEE Trans. On Image Processing, Vol.18(7), pp. 1665-1670, July 2009.

95. Jie Zhou, Fanglin Chen, Nannan Wu, and Chenyu Wu. Crease Detection from Fingerprint Images and Its Applications in Elderly People. Pattern Recognition Vol.42(5), pp.896–906, May, 2009.

96. Xi Wang, Chunyu Yang, and Jie Zhou, Clustering Aggregation by Probability Accumulation, Pattern Recognition Vol.42(5), pp.668–675, May, 2009

97. Dingrui Wan and Jie Zhou. Multi-resolution and wide-scope depth estimation using a dual-PTZ-camera system. IEEE Trans. On Image Processing, Vol.18(3), pp.677-682, 2009

98. Lin Zhu, Jie Zhou, Jingyan Song, Tracking Multiple Objects Through Occlusion with Online Sampling and Position Estimation, Pattern Recognition Vol.41(8), pp.2447–2460, Aug. 2008

99. Chunyu Yang and Jie Zhou. Non-stationary Data Sequence Classification Using Online Class Distribution Estimation. Pattern Recognition Vol.41(8), pp.2656–2664, Aug. 2008

100. Dingrui Wan and Jie Zhou. Stereo vision using two PTZ cameras. Computer Vision and Image Understanding. Vol.112, pp.184–194, Nov. 2008

101. J. Zhou, D. Gao, and D. Zhang, “Robust Moving Vehicle Detection for Automatic Traffic Monitoring”, IEEE Trans. On Vehicular Technology, Vol.56(1), pp.51-59, Jan, 2007

102. C. Yang, and J. Zhou, “A comparative study of combining multiple enrolled samples for fingerprint verification”, Pattern Recognition, 39(11): 2115-2130, 2006.

103. Z. Fan, J. Zhou and Y. Wu, “Multibody Grouping by Inference of Multiple Subspaces from High-Dimensional Data Using Oriented-Frames”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 28(1):.91-105, 2006.

104. D. Wan and J. Zhou, “Fingerprint Recognition Using Model-based Density Map”, IEEE Trans. On Image Processing, 15(6): 1690-1696, 2006.

105. J. Gu , J. Zhou, and Chunyu Yang, “Fingerprint Recognition by Combining Global Structure and Local Cues”, EEE Trans. On Image Processing, 15(7): 1952-1964, 2006.

106. Z. Fan, J. Zhou and Y. Wu, “Multibody Motion Segmentation Based on Simulated Annealing”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 776-781, USA, June 2004.

107. Z. Fan, J. Zhou and Y. Wu, “Inference of Multiple Subspaces from High -Dimensional Data and Application to Multibody Grouping”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 661-666, USA, June 2004.

108. J. Zhou, and J. Gu, “A Model-based Method for the Computation of Fingerprints Orientation Field”, IEEE Trans. On Image Processing, 13(6): 821-835, 2004.

109. J. Zhou and J. Gu, “Modeling Orientation Field of Fingerprints with Rational Complex Functions”, Pattern Recognition, 37(2): 389-391, 2004.

110. J. Gu, J. Zhou, and D. Zhang, “A combination Model for orientation Field of fingerprints”, Pattern Recognition, 37(3): 543-553, 2004. 

111. Y. Zhang, and J. Zhou, “Audio Segmentation Based On Multi-Scale Audio Classification”. Proceedings of International Conference of ASSP, Canada, 4: 349-352, 2004.

112. J. Gu and J. Zhou, “A Novel Model for Orientation Field of Fingertips”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, USA, 2: 493-498, 2003.

113. J. Zhou, L. Xin, and D. Zhang, “Scale-orientation histogram for texture image retrieval”, Pattern Recognition, 36(4): 1061-1063, 2003. 

114. C. Wu, J. Zhou, Z. Bian and G. Rong, “Detecting Creases in the Fingerprint”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, USA, June 2003, vol 2, pp. 505-510.

115. D. Zhang, H. Peng, J. Zhou, and S. K. Pal, “A Novel Face Recognition System Using Hybrid Neural and Dual Eigenspaces Methods”, IEEE Trans. On SMC, Part A, 32(6), 2002.

116. J. Zhou, X. Lu, D. Zhang and C. Wu, “Orientation analysis for rotated human face detection”, Image and Vision Computing, 20(4): 257-264, 2002.

117. J. Zhou, and D. Zhang, “Face Recognition by Combining Several Algorithms”, Proceedings of 16th International Conference on Pattern Recognition (ICPR2002), Canada, 3: 497-500, 2002.

118. D. Gao, J. Zhou, and L. Xin, “A novel algorithm of Adaptive background estimation”, Proceedings of 2001 International Conference on Image Processing, 2: 395-398, 2001.

119. J. Zhou, etal, “Automatic Cartridge Identification for Firearm authentication”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.1, USA, Dec 2001, pp749-754

120. X. Lv, J. Zhou, and C. Zhang.“A Novel Algorithm for Rotated Human Face Detection”, Proceedings of IEEE International Symposium of Computer Vision and Pattern Recognition, Vol.1, USA, June 2000, pp760-765

 

榮譽(yù)獎(jiǎng)勵(lì)：

1、1995年 獲得華中理工大學(xué)頒發(fā)的 華中理工大學(xué)優(yōu)秀博士論文獎(jiǎng)。

2、1998年 獲得教育部頒發(fā)的 教育部科技進(jìn)步獎(jiǎng)（應(yīng)用類）一等獎(jiǎng)。

3、2003年 獲得清華大學(xué)頒發(fā)的 清華大學(xué)青年教師教學(xué)優(yōu)秀獎(jiǎng)（全校12名）。

4、2004年 獲得清華大學(xué)頒發(fā)的 清華大學(xué)研究生良師益友。

5、2004年 獲得清華大學(xué)頒發(fā)的 清華大學(xué)教學(xué)優(yōu)秀成果二等獎(jiǎng)。

6、2005年 入選教育部新世紀(jì)優(yōu)秀人才計(jì)劃。

7、2005年 獲得清華大學(xué)頒發(fā)的 研究生“良師益友”（公共課教師部分，全校10名）。

8、2012年獲國家杰出青年科學(xué)基金。

9、2012年獲得寶鋼優(yōu)秀教師獎(jiǎng)（清華大學(xué)共5人獲獎(jiǎng)）。

10、2013年獲得IBM Faculty Award。

11、2013年獲得《自動(dòng)化學(xué)報(bào)》杰出奉獻(xiàn)編委獎(jiǎng)。

12、指導(dǎo)的博士生陳芳林獲2013年全國優(yōu)秀博士論文提名獎(jiǎng)。

13、指導(dǎo)的博士生代季峰獲2015年中國自動(dòng)化學(xué)會(huì)優(yōu)秀博士論文提名獎(jiǎng)。

14、指導(dǎo)的博士生胡瀚獲2016年中國人工智能學(xué)會(huì)優(yōu)秀博士論文。

15、2011年獲中國電子學(xué)會(huì)電子信息科學(xué)技術(shù)獎(jiǎng)一等獎(jiǎng)（排名第1）。

16、2011年獲高等學(xué)�？茖W(xué)研究優(yōu)秀成果（自然科學(xué)類）二等獎(jiǎng)（排名第1）。

17、2023年 榮獲北京市教育系統(tǒng)“育人榜樣(先鋒)”稱號(hào) 。

研究組：

智能視覺研究組

http://ivg.au.tsinghua.edu.cn/

智能視覺研究組隸屬于清華大學(xué)自動(dòng)化系信息處理研究所，同時(shí)也是信息科學(xué)與技術(shù)國家實(shí)驗(yàn)室（籌）和智能技術(shù)與系統(tǒng)國家重點(diǎn)實(shí)驗(yàn)室的組成部分。研究室現(xiàn)有教授4人、副教授1人、講師1人、博士后3名、博士生5名、碩士生10余名，研究室主任為周杰教授。

本研究組研究方向包括模式識(shí)別、人工智能、計(jì)算機(jī)視覺、機(jī)器學(xué)習(xí)等。在指紋識(shí)別、視頻監(jiān)控、基于視頻的公路、水路交通量統(tǒng)計(jì)分析、音樂內(nèi)容分析與識(shí)別、網(wǎng)絡(luò)圖片內(nèi)容敏感度分級(jí)檢測等方面進(jìn)行了卓有成效的研究工作，并與國家政府部門、國內(nèi)外著名大學(xué)、研究所、企事業(yè)單位等建立了密切的聯(lián)系。

Machine Learning 

Compact Representation Learning：

 

Deep Metric Learning ：

Deep Reinforcement Learning ：

 

Object Detection ：

 

Visual Tracking ：

 

Action Recognition ：

3D Vision ：

 

 

Face Recognition ：

 

Person Re-identification ：

 

Fingerprint Recognition ：

Palmprint Recognition ：

 

http://www.ssme.org.cn

現(xiàn)代服務(wù)科學(xué)與技術(shù)研究中心是在國家中長期規(guī)劃“大力發(fā)展現(xiàn)代服務(wù)業(yè)，改組和改造傳統(tǒng)服務(wù)業(yè)”的背景下，為了整合現(xiàn)有學(xué)科和發(fā)展交叉學(xué)科而成立的一個(gè)人才培養(yǎng)和學(xué)術(shù)研究基地，并由中國科學(xué)院院士、清華大學(xué)信息學(xué)院前任院長李衍達(dá)教授倡議并領(lǐng)導(dǎo)籌建。目前掛靠在清華大學(xué)自動(dòng)化系，已有近10位信息學(xué)科和管理學(xué)科的教授、副教授以及20多名博士生和碩士生參與中心的教學(xué)和學(xué)術(shù)研究。周杰教授擔(dān)任研究中心主任。

現(xiàn)代服務(wù)科學(xué)與技術(shù)研究中心的宗旨是針對現(xiàn)代服務(wù)業(yè)中的基礎(chǔ)科學(xué)和技術(shù)應(yīng)用問題，結(jié)合科技創(chuàng)新，聯(lián)合清華大學(xué)相關(guān)院系的專家學(xué)者以及國際著名大學(xué)、研究機(jī)構(gòu)和企業(yè)，展開深入和系統(tǒng)的科技研究。

研究中心在互聯(lián)網(wǎng)信息安全、信息管理、信息挖掘、系統(tǒng)建模、系統(tǒng)控制與優(yōu)化、電子政務(wù)、電子商務(wù)、現(xiàn)代物流、收益管理等方面有著多年的研究基礎(chǔ)。研究中心正在組織隊(duì)伍，規(guī)劃研究方向，初步考慮在海量數(shù)據(jù)的管理與監(jiān)控、信息挖掘、發(fā)布與組織、服務(wù)建模與性能管理、復(fù)雜系統(tǒng)優(yōu)化與控制、面向服務(wù)的高性能計(jì)算、服務(wù)創(chuàng)新模式及技術(shù)、電子金融、電子政務(wù)等方面展開研究。

研究中心不僅已與IBM中國研究院在人才培養(yǎng)和學(xué)術(shù)研究方面開展了全面而深入的合作，還得到了國家交通部等部門的大力支持，將在交通信息港建設(shè)和交通信息服務(wù)等方面進(jìn)行廣泛合作。目前，正在與國家科技部和建設(shè)銀行等單位洽談合作。與國際著名大學(xué)方面，已與香港中文大學(xué)達(dá)成建立聯(lián)合研究所的意向，并將與香港中文大學(xué)系統(tǒng)工程與工程管理系開展全面聯(lián)合。

研究中心將把人才培養(yǎng)作為一個(gè)重要目標(biāo)。在學(xué)校領(lǐng)導(dǎo)和信息學(xué)院的大力支持下，我們開始了“服務(wù)科學(xué)與技術(shù)”交叉學(xué)科的建設(shè)和人才培養(yǎng)工作。不僅從社會(huì)需求出發(fā)，發(fā)起和完成社會(huì)調(diào)研，形成《現(xiàn)代服務(wù)業(yè)發(fā)展及人才需求調(diào)查》分析報(bào)告；還在現(xiàn)有學(xué)科基礎(chǔ)上，擬定了《服務(wù)科學(xué)人才培養(yǎng)方案》和以現(xiàn)代服務(wù)業(yè)人才培養(yǎng)為目標(biāo)的教材體系方案；并且積極組織和邀請國內(nèi)、外服務(wù)科學(xué)方面的著名學(xué)者、專家進(jìn)行講學(xué)，與國際著名大學(xué)和著名國際企業(yè)合作，開設(shè)服務(wù)科學(xué)類課程，為國家現(xiàn)代服務(wù)業(yè)培養(yǎng)急需人才。