ICPRS 2019
 
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Keynotes
(We expect to announce more keynote speakers on hot topics in the field soon)

Keynote 1:
Prof. Sergio A Velastin is a Senior Research Scientist at Cortexica Vision Systems Ltd., UK. He was previously Conex Research Professor in the Applied Artificial Intelligence Research Group at the Universidad Carlos III in Madrid. He trained and worked most of his life in the UK where he became Professor in Applied Computer Vision at Kingston University and where he was also director of the Digital Imaging Research Centre. He is also a Fellow of the Institution of Engineering and Technology (IET) and Senior Member of the IEEE where he was member of the Board of Governors of the Intelligent Transportation Society (IEEE-ITSS). Sergio has worked for many years in the field of artificial vision and its application to improve public safety especially in public transport systems. He co-founded Ipsotek Ltd and has worked, on projects with transport authorities in London, Rome, Paris etc in several EU Framework Programme projects.

Human Action Detection: Computer vision research has been making steady progress for the last 20 years or so. In the last few years it has made much faster advances thanks to improved hardware, an explosion in the amount of visual data available and the application of deep neural networks. An area that is still a challenge is the automatic description of human actions from video, otherwise known as human action recognition or human action detection. This talk will present some ideas on how to address this problem.

Keynote 2:
Dr. Matthijs Douze is a research scientist at Facebook AI Research (FAIR) since 2015. He received a MS in computer science from the ENSEEIHT and a PhD (2004) in computer vision at the University of Toulouse. From 2005-2015 he was an engineer on the LEAR project-team at INRIA Grenoble. His main research topics are large-scale algorithms for computer vision and similarity search.

Similarity search for weakly supervised Machine Learning: The growth of the datasets terms of size and number of classes blurs the lines between the classification and instance search tasks. K-nn classification has several drawbacks that prevent it from being competitive in a classical supervised setting. However, it has advantages in a "not really" supervised setting, ie. where there are tags rather than classes, or multi-media annotations (freeform text, face ids, GPS coordinates, URLs, etc.) It also generalizes smoothly to on-line training and situations where there is no clear separation between a train and a test set. We will present a few computer vision works where similarity search is applied to unsupervised and weakly supervised learning in computer vision.

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Related Links

(c) Website: Sergio A Velastin/IET, 2009...