Autumn School MALVIC: Machine Learning and Vision for Industrial Applications

Bringing together pioneering international scientists in machine learning and computer vision.

Sist oppdatert: Sep 17, 2021

Machine learning and computer vision have the potential to significantly improve the automation and autonomy of many industrial applications (e.g. offshore, automotive, telecommunication, gaming, and multimedia) by enhancing the operational performance, decreasing cost related to manual operations, increasing benefits, minimizing losses, optimizing productivity and improving safety and security.

The goal of this Autumn School MALVIC is to bring together pioneering international scientists in machine learning and computer vision with both academia and practitioners from the industrial fields on a unique setting for the discussion and demonstration of practical, hands-on machine learning and vision research and development. Offshore industrial applications and industrial process scenarios are examples for the autumn school target.

Attendees of MALVIC will enjoy the following

  • World pioneer scientists giving lectures on computer vision and AI.
  • World companies (e.g. Google, Daimler, NVIDIA, Amazon) giving practical talks on Computer Vision and AI in practice
  • Business talks about few companies sharing their success stories on how did they grow and make money using computer vision and AI.
  • A surprise session that we are working on.

Registration

Early bird registration deadline: 17th September, 2021

Regular registration deadline: 12th October, 2021

Download Autumn School flyer
Download Program (Tentative)

Invited speakers (Tentative)

Guy Theraulaz: Collective Intelligence “the Ant Model”

Guy Theraulaz

Prof. Guy Theraulaz, CNRS Research Director, France, is a world-leading expert in the study of collective intelligence and collective behaviors in animal and human groups. He is also a leading researcher in the field of swarm intelligence and computational biology, primarily studying social insects but also distributed algorithms, e.g. for collective robotics, directly inspired by nature.

Jürgen Schmidhuber: Modern Artificial Intelligence - 1980s-2021 and Beyond

Jurgen Schmidhuber

Prof. Jürgen Schmidhuber, Scientific Director of IDSIA, Switzerland, is a computer scientist most noted for his work in the field of artificial intelligence, deep learning and artificial neural networks. He is a co-director of the Dalle Molle Institute for Artificial Intelligence Research in Manno, in the district of Lugano, in Ticino in southern Switzerland.

René Vidal: Mathematics of Deep Learning

Rene Vidal

Prof. René Vidal, Professor at JHU, USA, and Chief scientist at NORCE. He is the Herschel Seder Professor of Biomedical Engineering and the Inaugural Director of the Mathematical Institute for Data Science at The Johns Hopkins University. He has secondary appointments in Computer Science, Electrical and Computer Engineering, and Mechanical Engineering.

Thomas Bäck: Evolutionary Learning

Thomas Back

Prof. Thomas Bäck, Professor at Leiden University (The Netherlands) and Chief Scientist at NORCE. He is head of the Natural Computing Research Group and Director of Education at the Leiden Institute of Advanced Computer Science (LIACS). He received his PhD in Computer Science from Dortmund University, Germany, in 1994. He has been Associate Professor of Computer Science at Leiden University since 1996 and full Professor for Natural Computing since 2002.

Horst Bischof: Understanding Activities in an Industrial Context

Horst Bischof

This talk will highlight some recent work in the area of understanding actions and human activities. Special emphasis will be devoted to sequence segmentation and recognition of complex (long-term) Activities and domain adaptation. Examples from real world applications will illustrate the presented methods.

Prof. Horst Bischof is vice rector for research and Professor at the Institute for Computer Graphics and Vision at the Graz University of Technology, Austria. He has more than 750 publications with notable works on object recognition, visual learning, on-line and life-long learning, motion and tracking, visual surveillance and biometrics and medical computer vision.

Daniel Cremers: Deep Visual SLAM

Daniel Cremers

Visual Simultaneous Localization and Mapping (SLAM) is of utmost importance to autonomous systems and augmented reality. I will discuss direct methods for visual SLAM (LSD SLAM and DSO) that recover camera motion and 3D structure directly from brightness consistency thereby providing better performance in terms of precision and robustness compared to classical keypoint-based techniques.

Moreover, I will demonstrate how we can leverage the predictive power of self-supervised deep learning in order to significantly boost the performance of direct SLAM methods. The resulting methods D3VO allow us to track a single camera with a precision that is on par with state-of-the-art stereo-inertial odometry methods.

Lastly, I will introduce MonoRec - a deep network that can generate faithful dense reconstruction of the observed world from a single moving camera.

Prof. Daniel Cremers is Professor of Informatics and Mathematics at TU Munich and Germany. He is one of the leading experts in computer vision, machine learning & deep networks with focus on mathematical image analysis (segmentation, motion estimation, multiview reconstruction, visual SLAM). In December 2010 he was listed among "Germany's top 40 researchers below 40" (Capital). On March 1st 2016, Prof. Cremers received the Gottfried Wilhelm Leibniz Award, the biggest award in German academia.

Takeo Kanade:

Takeo Kanade

Prof. Takeo Kanade, Professor, Carnegie Mellon University, USA. He is a Japanese computer scientist and one of the world’s foremorst scientists in computer vision. He has more than 300 publications and 20 patents and with notable works including Lucas-Kanade method, face detector, Tomasi-Kanade factorization method…etc.

Marius Leordeanu: Mining for Meaning. From Vision to Language

Marius Leordeanu

Prof. Marius Leordeanu,Professor, Politehnica University of Bucharest. He is also a Senior Scientist of the Romanian Academy (IMAR). He holds a PhD from the Robotics Institute of CMU and Bachelor’s in Computer Science and Mathematics in 2003, from Hunter College of the City University of New York.

Xin Yao: Evolutionary Computing

Xin-Yao

Xin Yao, Chair Professor, Southern University of Science and Technology, Shenzhen, China. He is also a part-time Professor of Computer Science at the University of Birmingham, UK. His major research interests include evolutionary computation, ensemble learning and search-based software engineering. His work won the 2001 IEEE Donald G. Fink Prize Paper Award; 2010, 2015 and 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards; 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist); 2011 IEEE Transactions on Neural Networks Outstanding Paper Award; and many other best paper awards. He received a prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013. He was recently selected to receive the 2020 IEEE Frank Rosenblatt Award.

Practical talks

Mathias Grundmann: Live Perception for Mobile & Web

Matthias_Grundmann

In this talk, I will present Machine Learning (ML) solutions for Live Perception developed by Google Research for mobile and web. Live Perception or Viewfinder ML comes with major technical challenges to enable ML on-device, in real-time and with low-latency. Once solved it enables applications like virtual beauty try-on in YouTube, AR Effects in Duo, gesture controls of devices and view-finder tracking for Google Lens and Translate. In this talk, we will cover the core-recipes behind Google’s Live Perception solutions, from model design to enabling ML infrastructure like MediaPipe and TFLite GPU acceleration. In particular we will be covering Face Meshes and iris tracking, Hand tracking and gesture control, body tracking for fitness applications and 3D object detection. The covered solutions are also available to the research and developer community via MediaPipe, —an open source, cross platform framework for building perception pipelines for mobile, web, desktop and python.

Matthias Grundmann is a Research Director in Google Research working in the area of Computer Vision, Machine Learning and Computational Video. He is leading a vertical team of ~40 Research and Software Engineers with focus on Machine Learning solutions for Live Perception (low-latency, on-device and real-time). His team develops high-quality, cross-platform ML solutions (MediaPipe) driven by GPU/CPU accelerated ML inference (TFLite GPU and XNNPack) for mobile and web. Among the rich portfolio of technologies his team develops are solutions for hand and body tracking, high-fidelity facial geometry and iris estimation, video segmentation, 2D object and calibration-free 6 DOF camera tracking, 3D object detection, Motion Photos and Live Photo stabilization.

Matthias received his Ph.D. from the Georgia Institute of Technology in 2013 for his work on Computational Video with focus on Video Stabilization and Rolling Shutter removal for YouTube. His work on Rolling Shutter removal won the best paper award at ICCP, 2012. He was recipient of the 2011 Ph.D. Google Fellowship in Computer Vision.

Stefano Soatto: Learning Representations

Stefano Soatto

Prof. Stefano Soatto, Professor, UCLA, Director of Applied Science, Amazon AI. He is a professor of computer science with notable works in Computer Vision and Nonlinear Estimation and Control Theory (vision, sound, touch) to interact with humans and the environment.

Fridtjof Stein: To be announced

Fridtjof Stein

Dr. Fridtjof Stein is a senior scientist at Daimler truck within the field of perception. He works for about three decades at Daimler in the field of autonomous driving in public traffic including real-time vision especially in the fields of stereo vision, optical flow, object detection, and ground modeling in the automotive domain.

Gal Chechik: Practical talk

Gal Chechik, Nvidia.

Business talks: Success Stories – How did AI Shape Your Business?

Introduction by chair Anne Grete Ellingsen, project manager national European Digital innovation Hub candidate. Short presentation of the EUs program on investment in digital infrastructure and the benefit for SMEs and start ups

Crayon presented by Geir Gulliksen

Intelecy presented by Espen Davidsen

Aquabyte presented by Trude Jansen Hagland

Rocket farm presented by Dan Peter Rye Moen

Idean presented by Lars Petter Aase

Workshop Organizers

The scientific committee

  • Nabil Belbachir, NORCE , Norway
  • René Vidal, Johns Hopkins University, USA
  • Thomas Bäck, Leiden University and NORCE
  • Marius Leordeanu, Politehnica University of Bucharest

The local team

  • Tonje Holand Salgado (Eyde Cluster)
  • Christian Von der Ohe (GCE NODE)
  • Anne Grete Ellingsen, (DIH Oceanopolis)
  • Magdalena Entner (NORCE)
  • Rune Rolvsjord (NORCE)
  • Inger-Lise Bergman (NORCE)

Supported by:

Gcenode
Nceeyde
Sinpro
Aihub
NORCE Logo Skjerm Blå
DIH Oceanopolis Gradient Aero Blue 2021 002