讲者信息：高新波，博士，教授，西安电子科技大学模式识别与智能系统学科带头人，综合业务网理论及关键技术国家重点实验室主任，国家万人计划科技创新领军人才，新世纪百千万人才工程国家级人选，国家杰出青年科学基金获得者，教育部长江学者特聘教授，科技部重点领域创新团队负责人、教育部创新团队负责人。IET Fellow、CIE Fellow、IEEE高级会员、中国图象图形学学会常务理事、中国计算机学会理事、中国指挥与控制学会富媒体指挥专委会常务委员、中国电子学会青年科学家俱乐部副主席。主要从事计算机视觉机器学习等领域的研究和教学工作，获国家自然科学二等奖1项、省部级科学技术一等奖3项。
讲者信息：韩军伟，西北工业大学教授，自动化学院副院长，信息融合技术教育部重点实验室副主任，国家优秀青年科学基金获得者，欧盟玛丽居里学者，陕西省重点科技创新团队负责人。主要研究方向是多媒体信息处理和脑成像分析。已在领域顶级期刊如：Proceedings of the IEEE，IEEE TPAMI, IJCV, NeuroImage, Cerebral Cortex等发表学术论文60余篇，在领域顶级的国际会议如：CVPR，ICCV，MICCAI，IPMI, IJCAI等发表学术论文30余篇。ESI高被引论文12篇，ESI热点论文5篇。获得国际会议ACM Multimedia 2010，MICCAI 2011和ICME 2016最佳学生论文奖提名。获得教育部自然科学二等奖（排名第一）等省部级科技奖励3项。担任IEEE TNNLS、IEEE THMS等6个国际期刊编委，担任ICPR, ACCV等多个国际会议的Area Chair.
讲者信息：纪荣嵘，福建省“闽江学者”特聘教授，厦门大学教授、博士生导师、2014年获国家优青，2016年获国家万人计划青年拔尖。主要研究方向为计算机视觉与多媒体技术。相关工作发表于SCI源期刊论文90余篇，包括ACM汇刊与IEEE汇刊近50篇、CCF A类国际会议长文40余篇。论文的Google Scholar引用次数近5000次，SCI引用1600余次，H-因子为33，12篇论文入选ESI高被引/热点论文；近年来主持国家自然科学基金联合重点项目、军委科技委战略前沿专项，国家重点研发计划课题/子课题等；获2007年微软学者奖、2011年ACM Multimedia最佳论文奖、2012年哈工大优秀博士论文、2015年省自然科学二等奖、2016年教育部技术发明一等奖。担任多个国际期刊的副编辑，VALSE 2017大会主席、ACM/IEEE高级会员。
报告摘要：It goes without saying that the success of deep learning is amazing. The rise of deep learning not only leads to a wave of breakthroughs in traditional AI areas, e.g. speech recognition and computer vision, but also opens up a number of possibilities that are unimaginable before — AI can now play chess games, perform cancer diagnosis, and even drive a car. Despite all such successful stories, the “intelligence” of most deep networks remain rather restrictive — they are essentially doing A to B regression, just that they are doing it particularly well. In the past two years, I worked with a group of talented students on a series of problems, with an aim to move beyond the aforementioned limitations and thus extend the power of deep models to more application domains. Many of our studies revolve around an important theme, namely, learning deep models from structured data. Particularly, we develop new modeling frameworks for high-resolution images, event photos, structured scenes, activity videos, movies, relational databases, etc. All such data, despite their different natures, have an important aspect in common, that is, they all contain structures, i.e. components related to each other. Analysis of their inherent structures not only gives us deeper insights into these domains, but also results in more effective models and training strategies (e.g. self-supervised training that does not rely on external supervision). In this talk, I will give a high-level review of our efforts and achievements, and share my thoughts and reflections on the underlying problems.
讲者信息：Dahua Lin is an Assistant Professor at the department of Information Engineering, the Chinese University of Hong Kong. He received the B.Eng. degree from the University of Science and Technology of China (USTC) in 2004, the M. Phil. degree from the Chinese University of Hong Kong (CUHK) in 2006, and the Ph.D. degree from Massachusetts Institute of Technology (MIT) in 2012. Prior to joining CUHK, he served as a Research Assistant Professor at Toyota Technological Institute at Chicago, from 2012 to 2014. His research interest covers computer vision, machine learning, and big data analytics. In recent years, he primarily focused on deep learning and its applications on high-level visual understanding, probabilistic inference, and big data analytics. He has published about sixty papers on top conferences and journals, e.g. ICCV, CVPR, ECCV, NIPS, and T-PAMI. He serves as an Area Chair of ECCV 2018. His seminal work on a new construction of Bayesian nonparametric models has won the best student paper award in NIPS in year 2010. He also received the outstanding reviewer award in ICCV 2009 and ICCV 2011. He has supervised or co-supervised the CUHK team in international competitions and won multiple awards in ImageNet 2016, ActivityNet 2016, and ActivityNet 2017.
报告摘要：In this talk, I will describe some of our recent work on monocular perspective camera based reconstruction of the non-rigid 3D shape or an object or a complex scene. We aim to answer an open question in 3D vision: “is it possible to recover the 3D shape of a dynamic deformable object with a single moving camera?”. Traditional methods for dynamic 3D reconstruction often employ stereo-vision, or assume the scene (with deformable object) follows certain simple low-order linear model. Our new work removes such restrictions and shows that, under certain mild assumptions, monocular 3D reconstruction of a dynamic shape scene is possible. I will explain two approaches, one is for the recovery of 3D dynamic human pose using structured movement, and the other is a dense surface reconstruction method for complex dynamic scene. Both methods achieved superior performance on standard benchmarks datasets.
讲者信息：Dr. Hongdong Li is an Associate Professor/Reader with the Computer Vision Group of ANU (Australian National University) and Chief Investigator for Australian Centre for Robotic Vision (ACRV). His research interests include 3D computer vision, SFM/SLAM for robot navigation and autonomous vehicles, as well as the application of mathematical optimization in geometric vision. He graduated from Zhejiang University, and taught at the same university before joined the ANU as a research fellow since 2004. During 2009-2010 he was a senior researcher with NICTA (Canberra Labs) working on the “Australia Bionic Eyes” project. He was a visiting professor with Carnegie Mellon University in 2017. He served as the Area Chair for CVPR, ICCV, ECCV, BMVC and 3DV in the past; Associate Editor for IEEE Transactions on PAMI (T-PAMI); Program Co-Chair for ACCV 2018. Jointly with students and co-workers he won a number of prestigious awards in computer vision, which include the CVPR Best Paper Award and Marr Prize-Honorable Mention.
报告摘要：Optimization is an indispensable part of machine learning. There have been various optimization algorithms, typically introduced independently in textbooks and scatter across vast materials, making the beginners hard to have a global picture. In this talk, by explaining how to relax some aspects of optimization procedures I will briefly introduce some practical optimization algorithms in a systematic way.
讲者信息：Zhouchen LIN received the Ph.D. degree in applied mathematics from Peking University in 2000. He is currently a Professor with the Key Laboratory of Machine Perception, School of Electronics Engineering and Computer Science, Peking University. His research interests include computer vision, image processing, machine learning, pattern recognition, and numerical optimization. He is an area chair of ACCV 2009/2018, CVPR 2014/2016, ICCV 2015, and NIPS 2015, and senior program committee of AAAI 2016/2017/2018 and IJCAI 2016/2018. He is an Associate Editor of the IEEE Transactions on Pattern Analysis And Machine Intelligence and the International Journal of Computer Vision. He is an IAPR/IEEE fellow.
报告摘要：Structure in data provide rich information that helps to reduce the complexity and improves the effectiveness of a model. In this talk, an introduction will be given on the recent progress in using deep learning as a tool for modeling the structure in visual data. We show that observation in our problem are useful in modeling the structure of deep model and help to improve the effectiveness of deep models for many vision problems.
讲者信息：Wanli Ouyang received the PhD degree in the Department of Electronic Engineering, The Chinese University of Hong Kong. He is now a senior lecturer at the University of Sydney. His research interests include image processing, computer vision and pattern recognition. He is the first author of 7 papers on TPAMI and IJCV, and has published around 40 papers on top tier conferences like CVPR, ICCV and NIPS. ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is one of the most important grand challenges in computer vision. The team led by him ranks No. 1 in the ILSVRC 2015 and ILSVRC 2016. He receives the best reviewer award of ICCV. He has been the reviewer of many top journals and conferences such as IEEE TPAMI, TIP, IJCV, SIGGRAPH, CVPR, and ICCV. He is a senior member of the IEEE.
报告摘要：Neural networks with a memory capacity provide a promising approach to media understanding (e.g., Q-A and visual classification). In this talk, I will present how to utilize the information in external memory to boost media understanding. In general, the relevant information (e.g., knowledge instance and exemplar data) w.r.t the input data is sparked from external memory in the manner of memory-augmented learning. Memory-augmented learning is an appropriate method to integrate data-driven learning, knowledge-guided inference and experience exploration.
讲者信息：Fei Wu is a professor at the college of computer science, Zhejiang University. From October, 2009 to August 2010, Fei Wu was a visiting scholar at Prof. Bin Yu's group, University of California, Berkeley. Currently, he is the vice-dean of college of Computer Science, and the director of Institute of Artificial Intelligence of Zhejiang University. His research interests mainly include Artificial Intelligence, cross-media computing, and multimedia retrieval. He has won various honors such as the Award of National Science Fund for Distinguished Young Scholars of China (2016).
讲者信息：杨铭博士，地平线（Horizon Robotics）联合创始人 & 软件副总裁，Facebook 人工智能研究院创始成员之一。杨铭曾任 NEC 美国研究院高级研究员，专注于计算机视觉和机器学习领域研究，包括物体跟踪、人脸识别、海量图片检索、及多媒体内容分析。他在 Facebook 工作期间负责的深度学习研发项目 DeepFace 在业界产生重大影响，被多家媒体广泛报道，包括 Science Magazine、MIT Tech Review、Forbes 等。他领导 NEC-UIUC 团队参加 TRECVID08/09 视频监控事件检测评测，获得最佳成绩；参与 NEC 团队 ImageNet2010 大规模图像分类挑战，获得第一名。申请获得美国专利14项。杨铭毕业于清华大学电子工程系并获得工学学士和硕士学位，于美国西北大学电气工程与计算机科学系获得博士学位。他在顶级国际会议 CVPR/ICCV 发表论文20余篇，在顶级国际期刊 T-PAMI 上发表8篇论文，被引用超过6200次；多次担任 CVPR/ICCV/NIPS/ACMMM 等顶级国际会议程序委员会成员，T-PAMI/IJCV/T-IP 等顶级国际期刊审稿人。
报告摘要：The "Machine learning + Knolwedge" approaches, which combine meachine learning with domain knowledge, enable us to achieve start-of-the-art performances for many tasks of medical image recognition, segmentation and parsing. In this talk, we first present real success stories of such approaches. Then, we proceed to elaborate deep learning, a special, mighty type of machine learning method, and review its recent advances. We conclude with several latest "DL & Beyond" works.
讲者信息：Dr. S. Kevin Zhou is currently a Principal Key Expert of Image Analysis at Siemens Healthcare Technology, dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine learning and their applications to medical image recognition and parsing, face recognition and modeling, etc. Dr. Zhou has published over 150 book chapters and peer-reviewed journal and conference papers, has registered over 250 patents and inventions, has written two research monographs, and has edited three books. His two most recent books are entitled "Medical Image Recognition, Segmenation and Parsing: Machine Learning and Multiple Object Approaches, SK Zhou (Ed.)" and "Deep Learning for Medical Image Analysis, SK Zhou, H Greenspan, DG Shen (Eds.)." He has won multiple awards honoring his publications, patents and products, including Thomas Alva Edison Patent Award (2013), R&D 100 Award or Oscar of Invention (2014), Siemens Inventor of the Year (2014), and UMD ECE Distinguished Aluminum Award (2017). He has been an associate editor for IEEE Trans Medical Imaging and Medical Image Analysis journals， an area chair for CVPR and MICCAI, a co-Editor-in Chief for WeChat public journal The Vision Seeker, and elected as a fellow of American Institute of Biological and Medical Engineering (AIMBE).
讲者信息：朱军，清华大学长聘副教授、卡内基梅隆大学兼职教授、智能技术与系统国家重点实验室副主任。2001到2009年获清华大学计算机学士和博士学位，之后在卡内基梅隆大学做博士后，2011年回清华任教。主要从事人工智能基础理论、高效算法及相关应用研究，在国际重要期刊与会议发表学术论文近百篇。受邀担任人工智能顶级杂志IEEE TPAMI和AI的编委、《自动化学报》编委，担任机器学习国际大会ICML2014地区联合主席, ICML (2014-2018)、NIPS (2013, 2015)、UAI (2014-2017)、IJCAI（2015,2017）、AAAI（2016-2018）等国际会议的领域主席，中国计算机学会（CCF）学术工委主任助理。获CCF优秀博士论文奖、CCF自然科学一等奖、CCF青年科学家奖、国家优秀青年基金、北京市优秀青年人才奖、清华大学优秀班主任一等奖等，入选国家“万人计划”青年拔尖人才、IEEE Intelligent Systems杂志评选的“AI’s 10 to Watch”及清华大学221基础研究人才计划。指导的学生获NIPS 2017国际人工智能对抗攻防竞赛全部三个任务的冠军。