中国人民大学信息学院硕士研究生导师信息：卢志武

　　考研中一般在复试期间大家会联系硕士研究生导师，因为提前联系运气好的话，导师看到你的简历后可能对你非常感兴趣，在不违背原则的前提下没准会对你的复试指点一二。那在和导师邮件沟通的过程中如果你对导师的学术著作颇有研究或者在考研前就已经瞄准某位导师，那就很有必要对于硕士研究生导师的信息提前熟悉了解，方便以后的沟通。下面新东方在线考研频道为大家分享：“ 中国人民大学信息学院硕士研究生导师信息：卢志武 ”文章。

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　　卢志武

　　卢志武博士，中国人民大学高瓴人工智能学院教授，博士生导师。2005年7月毕业于北京大学数学科学学院信息科学系，获理学硕士学位;2011年3月毕业于香港城市大学计算机系，获PhD学位。主要研究方向包括机器学习、计算机视觉等。主持NSFC、KJW等多个国家项目。设计首个公开的中文通用图文预训练模型BriVL。参与的留守儿童心理健康调研报告获总理重要批示。以主要作者身份发表学术论文70余篇，其中在TPAMI、IJCV、TIP等重要国际期刊和ICLR、NeurIPS、CVPR、ICCV、ECCV等重要国际会议上发表论文40余篇，CCF A类论文30篇。获ImageNet 2015视频检测亚军、2015年IBM SUR Award、CGI 2014最佳论文奖等。担任CCF生物信息学专委会委员。担任NeurIPS、ICML、ICCV、CVPR、AAAI、IJCAI等国际顶级会议的程序委员。

　　电子邮箱：luzhiwu[AT]ruc.edu.cn

　　更多教育经历

　　2002年7月，毕业于武汉大学数学与统计学院数学与应用数学专业，获理学学士学位

　　2005年7月，毕业于北京大学数学科学学院信息科学系，获理学硕士学位

　　2011年3月，毕业于香港城市大学计算机系，获PhD学位

　　工作经历

　　2020年9月—现在，中国人民大学高瓴人工智能学院，教授

　　2019年8月—2020年8月，中国人民大学信息学院，教授

　　2013年9月—2019年7月，中国人民大学信息学院，副教授

　　2011年5月—2013年7月，北京大学计算机所，助理研究员

　　研究方向

　　机器学习：元学习，小样本学习，自监督学习，网络结构搜索，机器学习理论

　　计算机视觉：跨模态对比学习，文生成图，视频自监督表示学习，视频动作迁移，图像语义分割

　　应用研究：大规模多模态预训练(设计首个公开的中文通用图文预训练模型BriVL)，心理健康快速评估(参与的留守儿童心理健康调研报告获总理重要批示)

　　讲授课程

　　研究生课：《高级机器学习》

　　本科生课：《统计学习》，《分布式系统与云计算》

　　科研项目

　　◾ 阿里达摩院合作项目，面向视觉语言的多模态特征学习，2020.09-2021.09，主持

　　◾ 国家自然科学基金面上项目，小样本学习关键问题研究，2020.01-2023.12，主持

　　◾ KJW主题项目，基于知识分类的复杂目标渐近识别，2018.11-2020.09，主持

　　◾ 国家自然科学基金面上项目，噪声环境下的弱监督图像语义分割研究，2016.01-2019.12，主持

　　◾ 中国人民大学预研委托项目，噪声环境下的弱监督图像语义分割关键问题研究，2015.01-2017.12，主持

　　◾ 国家自然科学基金青年项目，基于图的半监督学习关键问题研究及其在图像理解中的应用，2013.01-2015.12，主持

　　◾ 北京市自然科学基金面上项目，基于稀疏表示的半监督学习新方法及应用研究，2013.01-2015.12，主持

　　科研成果

　　(一)代表性期刊论文：

　　◾ Jiechao Guan, Zhiwu Lu*, Tao Xiang, Aoxue Li, An Zhao, and Ji-Rong Wen, Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 43, no. 7, pp. 2510-2523, 2021. (CCF A)

　　◾ Yulei Niu, Hanwang Zhang, Zhiwu Lu, and Shih-Fu Chang, Variational Context: Exploiting Visual and Textual Context for Grounding Referring Expressions, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 43, no. 1, pp. 347-359, 2021. (CCF A)

　　◾ Aoxue Li, Zhiwu Lu*, Jiechao Guan, Tao Xiang, Liwei Wang, and Ji-Rong Wen, Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning, International Journal of Computer Vision (IJCV), vol. 128, no. 12, pp. 2810-2827, 2020. (CCF A)

　　◾ Yulei Niu, Zhiwu Lu*, Ji-Rong Wen, Tao Xiang, and Shih-Fu Chang, Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation, IEEE Transactions on Image Processing (TIP), vol. 28, no. 4, pp. 1720-1731, 2019. (CCF A)

　　◾ Aoxue Li, Zhiwu Lu*, Liwei Wang, Peng Han, and Ji-Rong Wen, Large Scale Sparse Learning from Noisy Tags for Semantic Segmentation, IEEE Transactions on Cybernetics (TCYB), vol. 48, no. 1, pp. 253-263, 2018. (ESI高被引论文)

　　◾ Zhiwu Lu, Zhenyong Fu, Tao Xiang, Peng Han, Liwei Wang, and Xin Gao, Learning from Weak and Noisy Labels for Semantic Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 39, no. 3, pp. 486-500, 2017. (CCF A)

　　◾ Aoxue Li, Zhiwu Lu*, Liwei Wang, Tao Xiang, and Ji-Rong Wen, Zero-Shot Scene Classification for High Spatial Resolution Remote Sensing Images, IEEE Transactions on Geoscience and Remote Sensing (TGRS), vo. 55, no. 7, pp. 4157-4167, 2017.

　　◾ Xuefeng Cui, Zhiwu Lu, Sheng Wang, Jim Jing-Yan Wang, and Xin Gao, CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction, Bioinformatics, vol. 32, no. 12, pp. i332-i340, 2016.

　　◾ Zhiwu Lu, Peng Han, Liwei Wang, and Ji-Rong Wen, Semantic Sparse Recoding of Visual Content for Image Applications, IEEE Transactions on Image Processing (TIP), vol. 24, no. 1, pp. 176-188, 2015. (CCF A)

　　◾ Zhiwu Lu and Liwei Wang, Learning Descriptive Visual Representation for Image Classification and Annotation, Pattern Recognition (PR), vol. 48, no. 2, pp. 498-508, 2015.

　　◾ Zhiwu Lu and Liwei Wang, Noise-Robust Semi-Supervised Learning via Fast Sparse Coding, Pattern Recognition (PR), vol. 48, no. 2, pp. 605-612, 2015.

　　◾ Liuyang Zhou, Zhiwu Lu, Howard Leung, and Lifeng Shang, Spatial temporal pyramid matching using temporal sparse representation for human motion retrieval, The Visual Computer, vol. 30, no. 6-8, pp. 845-854, 2014. (Special Issue on CGI 2014, 最佳论文奖)

　　◾ Zhiwu Lu and Yuxin Peng, Exhaustive and Efficient Constraint Propagation: A Graph-Based Learning Approach and Its Applications, International Journal of Computer Vision (IJCV), vol. 103, no. 3, pp. 306-325, 2013. (CCF A)

　　◾ Zhiwu Lu and Yuxin Peng, Latent Semantic Learning with Structured Sparse Representation for Human Action Recognition, Pattern Recognition (PR), vol. 46, no. 7, pp. 1799-1809, 2013.

　　(二)代表性会议论文：

　　◾ Guoxing Yang, Nanyi Fei, Mingyu Ding, Guangzhen Liu, Zhiwu Lu*, and Tao Xiang, L2M-GAN: Learning to Manipulate Latent Space Semantics for Facial Attribute Editing, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. (Oral, CCF A)

　　◾ Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen, Counterfactual VQA: A Cause-Effect Look at Language Bias, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. (CCF A)

　　◾ Mingyu Ding, Xiaochen Lian, Linjie Yang, Peng Wang, Xiaojie Jin, Zhiwu Lu, and Ping Luo, HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. (Oral, CCF A)

　　◾ Nanyi Fei, Zhiwu Lu*, Tao Xiang, and Songfang Huang, MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning, International Conference on Learning Representations (ICLR), 2021.

　　◾ Manli Zhang, Jianhong Zhang, Zhiwu Lu*, Tao Xiang, Mingyu Ding, and Songfang Huang, IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning, International Conference on Learning Representations (ICLR), 2021.

　　◾ Yao Lu, Xiaoli Xu, Mingyu Ding, Zhiwu Lu*, and Tao Xiang, A Global Occlusion-Aware Approach to Self-Supervised Monocular Visual Odometry, AAAI Conference on Artificial Intelligence (AAAI), 2021. (CCF A)

　　◾ Yuqi Huo, Mingyu Ding, Haoyu Lu, Ziyuan Huang, Mingqian Tang, Zhiwu Lu*, and Tao Xiang, Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw, International Joint Conference on Artificial Intelligence (IJCAI), 2021. (CCF A)

　　◾ Yizhao Gao, Nanyi Fei, Guangzhen Liu, Zhiwu Lu*, and Tao Xiang, Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning, 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021.

　　◾ Mingyu Ding, Yuqi Huo, Hongwei Yi, Zhe Wang, Jianping Shi, Zhiwu Lu, and Ping Luo, Learning Depth-Guided Convolutions for Monocular 3D Object Detection, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2020. (CCF A)

　　◾ Mingyu Ding, Zhe Wang, Bolei Zhou, Jianping Shi, Zhiwu Lu*, and Ping Luo, Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow, AAAI Conference on Artificial Intelligence (AAAI), New York, USA, 2020. (CCF A)

　　◾ Mingyu Ding, An Zhao, Zhiwu Lu*, Tao Xiang, and Ji-Rong Wen, Face-Focused Cross-Stream Network for Deception Detection in Videos, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019. (CCF A)

　　◾ Yulei Niu, Hanwang Zhang, Manli Zhang, Jianhong Zhang, Zhiwu Lu*, and Ji-Rong Wen, Recursive Visual Attention in Visual Dialog, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019. (Oral, CCF A)

　　◾ Aoxue Li, Tiange Luo, Zhiwu Lu*, Tao Xiang, and Liwei Wang, Large-Scale Few-Shot Learning: Knowledge Transfer with Class Hierarchy, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019. (CCF A)

　　◾Yuqi Huo, Yao Lu, Yulei Niu, Zhiwu Lu*, and Ji-Rong Wen, Coarse-to-Fine Grained Classification, ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Paris, France, 2019. (CCF A)

　　◾ An Zhao, Mingyu Ding, Jiechao Guan, Zhiwu Lu*, Tao Xiang, and Ji-Rong Wen, Domain-Invariant Projection Learning for Zero-Shot Recognition, Thirty-Second Annual Conference on Neural Information Processing Systems (NIPS), Montréal, Canada, 2018. (CCF A)

　　◾ Yulei Niu, Zhiwu Lu*, Songfang Huang, Xin Gao, and Ji-Rong Wen, FeaBoost: Joint Feature and Label Refinement for Semantic Segmentation, AAAI Conference on Artificial Intelligence (AAAI), San Francisco, California, USA, 2017. (CCF A)

　　◾ Ruqi Zhang and Zhiwu Lu*, Large-Scale Sparse Clustering, International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, 2016. (CCF A)

　　◾ Zhiwu Lu, Xin Gao, Liwei Wang, Ji-Rong Wen, and Songfang Huang, Noise-Robust Semi-Supervised Learning by Large-Scale Sparse Coding, AAAI Conference on Artificial Intelligence (AAAI), Austin, Texas, USA, 2015. (CCF A)

　　◾ Yulei Niu, Zhiwu Lu*, Songfang Huang, Peng Han, and Ji-Rong Wen, Weakly Supervised Matrix Factorization for Noisily Tagged Image Parsing, International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, 2015. (CCF A)

　　◾ Zhiwu Lu, Xin Gao, Songfang Huang, Liwei Wang, and Ji-Rong Wen, Social Image Parsing by Cross-Modal Data Refinement, International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, 2015. (CCF A)

　　◾ Zhiwu Lu, Liwei Wang, and Ji-Rong Wen, Direct Semantic Analysis for Social Image Classification, AAAI Conference on Artificial Intelligence (AAAI), Quebec City, Canada, 2014. (CCF A)

　　◾ Zhiwu Lu and Yuxin Peng, Learning Descriptive Visual Representation by Semantic Regularized Matrix Factorization, International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, 2013. (CCF A)

　　◾ Zhiwu Lu and Yuxin Peng, Unified Constraint Propagation on Multi-View Data, AAAI Conference on Artificial Intelligence (AAAI), Bellevue, Washington, USA, 2013. (CCF A)

　　社会兼职

　　◾ Reviewer for IEEE TPAMI, IJCV, IEEE TIP, IEEE TMM, IEEE TNNLS, IEEE TKDE, TOIS

　　◾ SPC/PC Member of ICML, NeurIPS, CVPR, ICCV, ECCV, AAAI, IJCAI, UAI

　　◾ Member of CCF, ACM, IEEE

　　荣誉获奖

　　◾ 2021年设计首个公开的中文通用图文预训练模型BriVL

　　◾ 2021年参与的留守儿童心理健康调研报告获总理重要批示

　　◾ ICONIP 2018最佳学生论文奖亚军

　　◾ 深度学习权威评测ImageNet 2015视频检测任务亚军

　　◾ 2015年IBM SUR Award

　　◾ IJCAI-15竞赛(Repeat Buyers Prediction)第4名

　　◾ 中国人民大学2015届优秀学士学位论文指导老师

　　◾ 计算机图形学国际会议CGI 2014最佳论文奖

　　以上就是新东方在线考研频道为大家分享的文章：“中国人民大学信息学院硕士研究生导师信息：卢志武 ”。建议大家给导师发邮件题目直接写“姓名 xxx专业硕士自荐信”等，让硕士研究生导师一眼就能知道你的目的。内容主要分成两个部分：第一，要说明自己的情况。第二，要表明对老师研究方向的兴趣。