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.
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。