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北京交通大学硕士研究生导师信息:王涛

2024-12-04 22:58:00来源:网络

  在读研期间,所有与你读研相关的事情,可能都需要经过你的导师同意,所以说,选择导师真的很重要,也希望大家能够认真对待这件事,怎样才能选择适合自己的导师呢?这就要我们提前做足功课,尽可能多的搜集有关你准备报考的导师的信息,下面新东方在线考研频道为大家分享:“北京交通大学硕士研究生导师信息:王涛”文章。

  北京交通大学硕士研究生导师信息:王涛

  王涛

  博士、教授

  基本信息

  办公电话:51688566电子邮件: twang@bjtu.edu.cn

  通讯地址:北京交通大学计算机学院九教北525邮编:100044

  教育背景

  2013年1月,北京交通大学 计算机科学与技术专业 博士学位

  2004年4月,北京交通大学 计算机应用技术专业 硕士学位

  2001年7月,北京交通大学 计算机科学与技术专业 学士学位

  工作经历

  2021.12至今,北京交通大学计算机学院 教授

  2020.12至今,北京交通大学计算机学院 科学系教师党支部书记

  2016.12-2021.11,北京交通大学计算机学院 副教授

  2014.12-2015.12,美国天普大学计算机系 访问学者

  2006.12-2016.11,北京交通大学计算机学院 讲师

  2004.04-2006.11,北京交通大学计算机学院 助教

  研究方向

  机器学习与认知计算

  计算机技术

  软件工程

  人工智能

  大数据技术与工程

  数字媒体信息处理与智能分析

  新一代电子信息技术

  招生专业

  计算机科学与技术硕士

  计算机技术硕士

  软件工程硕士

  人工智能硕士

  大数据技术与工程硕士

  计算机科学与技术博士

  新一代电子信息技术(含量子技术等)硕士

  软件工程博士

  新一代电子信息技术(含量子技术等)博士

  人工智能博士

  计算机技术博士

  科研项目

  国家自然科学基金"面上项目":可信赖异质图神经网络研究,2024-01-01--2027-12-31,主持

  国家自然科学基金"面上项目":面向关系推理的图神经网络关键问题研究,2021-01-01--2024-12-31,主持

  国家自然科学基金"面上项目":基于多模态超图的社群图像检索研究,2017-01-01--2020-12-31,主持

  国家自然科学基金"青年项目":基于组合地图模型的图像检索算法研究,2014-01-01--2016-12-31,主持

  北京市自然科学基金“面上项目”:面向关系推理的深度神经网络模型及算法研究,2020-01-01--2022-12-31,主持

  国家级"科技委":XXXX系统构建与计算推演算法,2022-05-01--2023-06-30,主持

  国家级平台专项:面向移动通信网络的大图数据分析与挖掘算法研究,2022-04-01--2020-03-30,主持

  横向课题:交通产业元宇宙技术与应用发展趋势研究,2024-05-01--2024-12-31,主持

  横向课题:视频监控智能分析技术,2016-05-01--2016-12-31,主持

  基本科研业务费:面向AR的高精度目标跟踪技术研究,2018-04-01--2020-03-30,主持

  基本科研业务费:基于内容安全的视频分析技术,2015-01-01--2016-12-31,主持

  基本科研业务费:基于组合地图的图像匹配与检索算法研究,2012-03-01--2014-02-28,主持

  国家级"科技委":基于XXXX数据计算理论与方法,2022-08-29--2027-08-31,参加

  国家级"科技委":高动态微光战场环境下的目标感知与认知一体化技术研究,2020-08-01--2022-07-31,参加

  国家(工信部等)专项:自动驾驶模拟仿真平台,2021-07-01--2023-06-30,参加

  国家(工信部等)专项:工业互联网创新发展工程-工业企业侧安全数据采集设备,2019-08-01--2021-07-31,参加

  国家重点研发计划-课题:异构交通主体群体智能协同行为仿真分析与评估,2019-03-01--2021-12-31,参加

  国家重点研发计划-任务:社区基础数据采集、处理、应用、共享技术,2018-07-01--2021-06-30,参加

  教学工作

  本科课程:《面向对象程序设计与C++》。

  研究生课程:《机器视觉基础》。

  论文/期刊

  Google Scholar:

  https://scholar.google.com/citations?user=F3C5oAcAAAAJ&hl=zh-CN

  2023:

  [1] F Luo, J Wu, T Wang. Discrete Listwise Content-aware Recommendation. ACM Transactions on Knowledge Discovery from Data, 2023.

  [2] H Liu, T Wang, Y Li, C Lang, Y Jin, H Ling. Joint graph learning and matching for semantic feature correspondence. Pattern Recognition, 2023.

  [3] Z Xu, L Wei, C Lang, S Feng, T Wang, AG Bors, H Liu. SSR-Net: A Spatial Structural Relation Network for Vehicle Re-identification. ACM Transactions on Multimedia Computing, Communications and Applications, 2023.

  [4] K Li, H Liu, T Wang. Centroid-based graph matching networks for planar object tracking. Machine Vision and Applications, 2023.

  [5] H Liu, X You, T Wang, Y Li. Object detection via inner-inter relational reasoning network. Image and Vision Computing, 2023.

  2022:

  [1] G Zhao, T Wang, Y Li, Y Jin, C Lang, S Feng. Neighborhood Pattern Is Crucial for Graph Convolutional Networks Performing Node Classification. IEEE Transactions on Neural Networks and Learning Systems, 2022.

  [2] F Luo, J Wu, T Wang. Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback. IJCAI, 2022.

  [3] XT You, H Liu, T Wang, S Feng, C Lang. Object detection by crossing relational reasoning based on graph neural network. Machine Vision and Applications. 2022.

  [4] T Liang, Y Jin, W Liu, S Feng, T Wang, Y Li. Keypoint-Guided Modality-Invariant Discriminative Learning for Visible-Infrared Person Re-identification. ACM MM, 2022.

  [5] Z Zhang, Y Jin, S Feng, Y Li, T Wang, H Tian. FENet: An Efficient Feature Excitation Network for Video-based Human Action Recognition. ICSP, 2022.

  [6] X Li, T Liang, Y Jin, T Wang, Y Li. Camera-Aware Style Separation and Contrastive Learning for Unsupervised Person Re-Identification. ICME, 2022.

  [7] X Deng, S Feng, G Lyu, T Wang, C Lang. Beyond word embeddings: Heterogeneous prior knowledge driven multi-label image classification. IEEE Transactions on Multimedia, 2022.

  [8] L Wei, C Lang, L Liang, S Feng, T Wang, S Chen. Weakly supervised video object segmentation via dual-attention cross-branch fusion. ACM Transactions on Intelligent Systems and Technology , 2022.

  2021:

  [1] Z Li, C Lang, T Wang, Y Li, J Feng. Deep spatio-frequency saliency detection. Neurocomputing, 2021, 453:645-655.

  [2] G Lyu, S Feng, Y Jin, T Wang, C Lang, Y Li. Prior Knowledge Regularized Self-Representation Model for Partial Multilabel Learning. IEEE Transactions on Cybernetics, 2021.

  [3] G Zhao, T Wang, Y Li, C Lang. Entropy-aware Self-training for Graph Convolutional Networks. Neurocomputing, 2021.

  [4] Z Xu, L Wei, C Lang, S Feng, T Wang, AG Bors. HSS-GCN: A Hierarchical Spatial Structural Graph Convolutional Network for Vehicle Re-identification. ICPR, 2021.

  [5] M Wang, C Lang, L Liang, G Lyu, S Feng, T Wang. Class-balanced Text to Image Synthesis with Attentive Generative Adversarial Network. IEEE MultiMedia, 2021.

  [6] M Wang, C Lang, S Feng, T Wang, Y Jin, Y Li. Text to photo-realistic image synthesis via chained deep recurrent generative adversarial network. Journal of Visual Communication and Image Representation, 2021.

  2020:

  [1] T Wang, H Liu, Y Li, Y Jin, H Ling*. Learning Combinatorial Solver for Graph Matching. CVPR, 2020. (oral)

  [2] G Lyu, S Feng, T Wang, C Lang. A Self-Paced Regularization Framework for Partial-Label Learning. IEEE Transactions on Cybernetics, 2020.

  [3] M Wang, C Lang, L Liang, S Feng, T Wang, Y Gao. End-to-End Text-to-Image Synthesis with Spatial Constrains. ACM Transactions on Intelligent Systems and Technology (TIST), 2020, 11(4):1-19.

  [4] M Wang, C Lang, L Liang, G Lyu, S Feng, T Wang. Attentive Generative Adversarial Network To Bridge Multi-Domain Gap For Image Synthesis. ICME, 2020, pp. 1-6.

  [5] Z Li, Y Jin, Y Li, C Lang, S Feng, T Wang. Learning part-alignment feature for person re-identification with spatial-temporal-based re-ranking method. World Wide Web, 23(3):1907-1923.

  [6] Y Li, K Liu, Y Jin, T Wang, W Lin. VARID: Viewpoint-aware re-identification of vehicle based on triplet loss. IEEE Transactions on Intelligent Transportation Systems. 2020.

  [7] T Liang, Y Jin, Y Li, T Wang. EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising. ICSP, 2020.

  2019:

  [1] T Wang, H Ling*, C Lang and S Feng. Deformable Surface Tracking by Graph Matching. ICCV, 2019.

  [2] L Sun, S Feng, T Wang, C Lang and Y Jin. Partial Multi-Label Learning by Low-Rank and Sparse Decomposition. AAAI, 2019.

  [3] G Lyu, S Feng, T Wang*, C Lang, Y Li. GM-PLL: Graph Matching based Partial Label Learning. IEEE Trans. on KDE, 2019. (online avaliable)

  [4] Z Li, C Lang, J Feng, Y Li, T Wang, S Feng. Co-saliency Detection with Graph Matching, ACM Trans. on TIST, 10(3): 22-30. 2019.

  [5] M Yin, C Lang, Z Li, S Feng, T Wang. Recurrent convolutional network for video-based smoke detection, Multimedia Tools and Applications, 78(1):237-256, 2019.

  [6] C Qian, Y Jin, Y Li, C Lang, S Feng, T Wang. Deep Domain Adaptation for Asian Face Recognition via Ada-IBN. ICMEW, 2019.

  [7] Z Li, Y Jin, Y Li, C Lang, S Feng, T Wang. Learning part-alignment feature for person re-identification with spatial-temporal-based re-ranking method. WWW, 2019.

  [8] J Zhou, T Wang, Y Jin. The hypergraph matching based on CCRP. BESC, 2019.

  2018:

  [1] T Wang, H Ling*. Gracker: A Graph-based Planar Object Tracker. IEEE Trans. on PAMI. 40(6):1494-1501, 2018.

  [2] T Wang, H Ling*, C Lang and S Feng. Branching and Adaptive Path Following for Graph Matching. IEEE Trans. on PAMI. 40(12):2853-2867, 2018.

  [3] T Wang, H Ling*, C Lang and S Feng. Constrained confidence matching for planar object tracking. ICRA, 2018.

  [4] J Zhou, T Wang*, C Lang, S Feng, Y Jin. A novel hypergraph matching algorithm based on tensor refining. Journal of Visual Communication and Image Representation, 57:69-75, 2018.

  [5] S Xu, T Wang*, C Lang, S Feng, Y Jin. Graph-based visual odometry for VSLAM. Industrial Robot: An International Journal, 45(5):679-687, 2018.

  [6] Z Li, C Lang, S Feng, T Wang. Saliency ranker: A new salient object detection method. Journal of Visual Communication and Image Representation, 50:16-26, 2018.

  [7] D Xu, C Lang, S Feng, T Wang. A framework with a multi-task CNN model joint with a re-ranking method for vehicle re-identification. ICIMCS, 2018.

  [8] K Yu, C Lang, S Feng, T Wang. Reasonably assign label distributions to GAN images in person re-identification baseline. BigMM, 2018.

  [9] X Xu, Y Li, Y Jin, C Lang, S Feng, T Wang. Hierarchical Discriminant Feature Learning for Heterogeneous Face Recoginition. VCIP, 2018.

  2017:

  [1] S Feng, C Lang, J Feng, T Wang, J Luo. Human facial age estimation by cost-sensitive label ranking and trace norm regularization, IEEE Transactions on Multimedia, 19(1):136-148, 2017.

  [2] R Chen, C Lang, T Wang*. Multiple path exploration for graph matching, Machine Vision and Applications, 28(7): 695-703, 2017.

  [3] Y Chen, T Wang*. Recursive formulas for embedding distributions of cubic outerplanar graphs, Australasian Journal of Combinatorics, 68(1):131-146, 2017.

  2016:

  [1] Tao Wang*, Haibin Ling, Congyan Lang, Jun Wu. Branching path following for graph matching. ECCV, 2016.

  [2] Tao Wang*, Haibin Ling. Path following with adaptive path estimation for graph matching. AAAI, 2016.

  [3] Tao Wang*, Haibin Ling, Congyan Lang, Songhe Feng. Symmetry-aware graph matching. Pattern Recognition, 60: 657-668, 2016.

  [4] Zhu Teng, Tao Wang, Feng Liu, et al., From samples selection to model update: A robust online visual tracking algorithm against. Neurocomputing. 173: 1221-1234, 2016.

  Earlier:

  [1]. Tao Wang*, Guojun Dai, Bingbing Ni, D. Xu. A distance measure between labeled combinatorial maps. Computer Vision and Image Understanding. 116(6): 1168-1177, 2012.

  [2]. Tao Wang*, Hua Yang, Congyan Lang, S. Feng. An error-tolerant approximate matching algorithm for labeled combinatorial maps. Neurocomputing. 156: 211-220, 2015.

  [3]. Tao Wang*, Guojun Dai, De Xu. A polynomial algorithm for submap isomorphism of general maps. Pattern Recognition Letters. 32(8): 1100-1107, 2011.

  [4]. Tao Wang*, Yanpei Liu. Implements of some new algorithms for combinatorial maps. OR Transactions. 12(2): 58-66, 2008.

  [5]. Tao Wang*, Congyan Lang, Songhe Feng. Joint tree of combinatorial maps. PAKDD 2014.

  [6]. Tao Wang*, Weisheng Li. Fast low-cost shortest path tree algorithm. Journal of Software. 15(2): 660-665, 2004.

  [7]. Tao Wang*, Weisheng Li. Shortest path subgraph. Journal of Northern Jiaotong University. 28(2):46-49, 2004.

  [8]. Yichao Chen, Yanpei Liu, Tao Wang. The Total Embedding Distributions of Cacti and Necklaces. Acta Mathematica Sinica, English Series. Vol. 22, no. 5, pp. 1583-1590, 2006.

  [9]. Shu Liu, Weisheng Li, Tao Wang. Advanced algorithm for fast lower-cost shortest path tree. Journal of Electronics and Information Technology. Vol. 27, no. 4, pp. 638-641, 2005.

  以上就是小编为大家分享的:“北京交通大学硕士研究生导师信息:王涛”,更多研究生导师信息,欢迎继续浏览新东方在线研究生导师频道。


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