特邀报告

 

 

Qi Tian

UTSA

陈宝权

北京大学/山东大学

吴飞

浙江大学

 

 

讲者介绍:

Qi Tian

University of Texas at San Antonio

Title:  Person Re-Identification: Recent Advances and Challenges

Abstract: As a research topic attracting more and more interests in both academia and industry, person Re-Identification (ReID) targets to identify the re-appearing persons from a large set of videos. It is potential to open great opportunities to address the challenging data storage problems, offering an unprecedented possibility for intelligent video processing and analysis, as well as exploring the promising applications on public security like cross camera pedestrian searching, tracking, and event detection.

     This talk aims at reviewing the latest research advances, discussing the remaining challenges in person ReID, and providing a communication platform for researchers working on or interested in this topic. This talk includes several parts on person ReID:

  • Wide deep models for fine-grained pattern recognition
  • Local and global representation learning for person ReID
  • The application of Generative Adversarial Networks in person ReID
  • Open issues and promising research topics of person ReID

     This talk also covers our latest work on person ReID, as well as our viewpoints about the unsolved challenging issues in person ReID. We believe this talk would be helpful for researchers working on person ReID and other related topics.

Bio: Qi Tian is currently a Full Professor in the Department of Computer Science, the University of Texas at San Antonio (UTSA). He was a tenured Associate Professor from 2008-2012 and a tenure-track Assistant Professor from 2002-2008. During 2008-2009, he took one-year Faculty Leave at Microsoft Research Asia (MSRA) as Lead Researcher in the Media Computing Group.

     Dr. Tian received his Ph.D. in ECE from University of Illinois at Urbana-Champaign (UIUC) in 2002 and received his B.E. in Electronic Engineering from Tsinghua University in 1992 and M.S. in ECE from Drexel University in 1996, respectively. Dr. Tian’s research interests include multimedia information retrieval, computer vision, machine learning and pattern recognition and published over 410 refereed journal and conference papers (including 106 IEEE/ACM Transactions papers and 76 CCF Category A conference papers). His Google Citation is 10000+ with h-index 54. He was the co-author of a Best Paper in ACM ICMR 2015, a Best Paper in PCM 2013, a Best Paper in MMM 2013, a Best Paper in ACM ICIMCS 2012, a Top 10% Paper Award in MMSP 2011, a Best Student Paper in ICASSP 2006, and co-author of a Best Student Paper Candidate in ICME 2015, and a Best Paper Candidate in PCM 2007.

     Dr. Tian research projects are funded by ARO, NSF, DHS, Google, FXPAL, NEC, SALSI, CIAS, Akiira Media Systems, HP, Blippar and UTSA. He received 2017 UTSA President’s Distinguished Award for Research Achievement, 2016 UTSA Innovation Award, 2014 Research Achievement Awards from College of Science, UTSA, 2010 Google Faculty Award, and 2010 ACM Service Award. He is the associate editor of IEEE Transactions on Multimedia (TMM), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Multimedia System Journal (MMSJ), and in the Editorial Board of Journal of Multimedia (JMM) and Journal of Machine Vision and Applications (MVA).  Dr. Tian served as Area Chairs for a number of conferences including CVPR, ICCV, ECCV, and ACM MM. Dr. Tian is a Fellow of IEEE.


陈宝权

北京大学/山东大学

题目:基于机器人的三维感知

报告简介:三维感知是实现机器人(包括无人机和任何移动智能体)与场景交互的要素,涉及到场景三维数据的获取、几何建模与理解,和基于三维的数据融合与智能分析。我将介绍近5年来本研究团队基于机器人的三维感知,特别是引入主动式(proactive)感知方法,构建数据获取与数据处理的闭环。该方向的研究,也从一个侧面展示了图形与图像技术之间越来越深度的融合。

个人介绍:北京大学前沿计算研究中心执行主任,信息科学技术学院教授,长江学者,杰青,兼山东大学特聘教授。纽约州立大学计算机博士。研究领域为计算机图形学与数据可视化。现任/曾任ACM TOG/IEEE TVCG编委、IEEE VIS/SIGGRAPH Asia指导委员会成员,曾任IEEE Vis 2005、ACM SIGGRAPH Asia 2014大会主席。获2003年美国NSF CAREER Award,2005年IEEE可视化国际会议最佳论文奖,和2014年中国计算机图形学杰出奖。担任973项目“城市大数据计算理论与方法”首席科学家,并任北京电影学院未来影像高精尖创新中心首席科学家。


吴飞

浙江大学

题目:数据、知识和行为交互下的智能学习

报告简介:人工智能中知识引导方法长于推理(但是其难以拓展)、数据驱动模型擅于预测识别(但是其过程难以理解)、策略学习手段能对未知空间进行探索(但其依赖于搜索策略)。本报告将探讨数据驱动中归纳、知识指导下演绎和行为强化内规划相互融合而进行智能学习途径。

个人介绍:浙江大学求是特聘教授、博士生导师。主要研究领域为人工智能、跨媒体计算、多媒体分析与检索和统计学习理论。浙江大学计算机学院副院长、浙江大学人工智能研究所所长。国家杰出青年科学基金获得者(2016年)、教育部新世纪优秀人才支持计划入选者(2011年)。在浙江大学新星计划资助下,于2009年10月至2010年8月在美国科学院院士、美国加州大学伯克利分校统计系郁彬(Bin Yu)教授课题组做访问学者。曾获浙江省自然科学一等奖1项,主持国家自然科学基金重点项目1项、973课题1项。