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PK10技巧关于举办西澳大学Ajmal Saeed Mian教授学术报告会的快3技巧通知

发布时间:2019-11-07设置

 
报告题目一Introduction to Artificial Intelligence with Deep Learning
报 告 人: Prof. Ajmal Saeed Mian
主 持 人:陈俊颖
报告时间:2019年11月6日(周三)下午14:30-16:00
报告地点:广西快3app下载大学城校区B8副楼报告厅
 
报告摘要:
Deep learning has achieved breakthough performances for many tasks often surpassing humans. In this talk, I will introduce the basics of deep learning starting from Multi Layer Perceptron and finishing on a few famous Convolutional Neural Network (CNN) architectures. Along the way, I will explain the main concepts of neural networks, what they actually learn, where their power comes form, how they avoid local minima, why greater depth is better and how to design very deep network architectures while avoiding the vanishing gradient problem. This talk assumes no prior knowledge of deep learning and is hence suitable for students at all stages of Engineering.
 
报告题目二:Deformable 3D face modeling for deep 3D face recognition and medical applications
报 告 人: Prof. Ajmal Saeed Mian
主 持 人:陈俊颖
报告时间:2019年11月7日(周四)下午14:30-16:00
报告地点:广西快3app下载大学城校区B8副楼报告厅
 
报告摘要:
In this talk, I will present our research on dense 3D face correspondence, a core problem for many applications such as biometric identification, symptomatology for the diagnosis of Autism and Obstructive Sleep Apnoea and planning for facial reconstructive surgery. From a morphometric point of view, we are interested in performing dense correspondence based purely on shape without using texture. This makes the problem challenging but the correspondences and subsequent analyses more precise. The idea is to start from a sparse set of automatically detected corresponding landmarks and propagate along the geodesics connecting nearby points. By anchoring on the most reliable correspondences for propagation, accurate dense correspondences are iteratively established between hundreds of faces without using a prior model. Thus, we are able to construct population specific deformable face models for symptomatology and patient specific morphs to facial norms for reconstructive surgery. Moreover, by establishing dense correspondences between different facial identities and expressions, we synthesize millions of 3D faces with varying identities, expressions and poses to learn a deep Convolutional Neural Network (FR3DNet) for large scale 3D face recognition. FR3DNet achieves state-of-the-art results, outperforming existing methods in open-world and close-world face recognition, on a dataset four times the largest dataset reported in the existing literature. At the end, I will show how our methods are used to diagnose Obstructive Sleep Apnea, Autism Spectrum Disorder in children and for orthodontic surgery planning/analysis.
 
报告题目三:Precision Modeling of 3D Human Motion: Behaviour and Performance Analysis
报 告 人: Prof. Ajmal Saeed Mian
主 持 人:陈俊颖
报告时间:2019年11月8日(周五)上午10:00-11:30
报告地点:广西快3app下载大学城校区B8副楼报告厅
 
报告摘要:
Deep learning has achieved breakthough performances for many tasks often surpassing humans. In this talk, I will introduce the basics of deep learning starting from Multi Layer Perceptron and finishing on a few famous Convolutional Neural Network (CNN) architectures. Along the way, I will explain the main concepts of neural networks, what they actually learn, where their power comes form, how they avoid local minima, why greater depth is better and how to design very deep network architectures while avoiding the vanishing gradient problem. This talk assumes no prior knowledge of deep learning and is hence suitable for students at all stages of Engineering.
 
报告人简介:
Ajmal Mian is a Professor of Computer Science at The University of Western Australia. He has received two prestigious fellowships and several research grants from the Australian Research Council and the National Health and Medical Research Council of Australia with a combined funding of over $12 million. He was the West Australian Early Career Scientist of the Year 2012 and has received several awards including the Excellence in Research Supervision Award, EH Thompson Award, ASPIRE Professional Development Award, Vice-chancellors Mid-career Research Award, Outstanding Young Investigator Award, the Australasian Distinguished Doctoral Dissertation Award and various best paper awards. He is an Associate Editor of IEEE Transactions on Image Processing and the Pattern Recognition journal. He has also served or is serving as a Guest Editor for special issues in Remote Sensing, Neural Computing & Applications, PR, CVIU and CVIU. He is a General Chair of the International Conference on Digital Image Computing Techniques and Applications (DICTA) 2019. He was a General Chair of the Asian Conference on Computer Vision 2018, Program Chair of DICTA 2012 and Area Chair of WACV 2019, WACV 2018, ICPR 2016 and ACCV 2014. Ajmal Mian has supervised 13 PhD theses to completion and has published over 180 scientific papers in prestigious journals and conferences including IEEE TPAMI, IEEE TNNLS, IEEE TIP, PR, IEEE TGRS, IEEE TITS, IEEE TBME, CVPR, ICCV and ECCV. His research interests are in computer vision, machine learning including defence against adversarial attacks, 3D shape and point cloud analysis, facial recognition, human action recognition and video analysis.
 
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