Professor Cipolla has been a Professor of Information Engineering at the University of Cambridge since 2000. Previously he worked as a Toshiba Fellow and engineer at the Toshiba Corporation Research and Development Centre in Kawasaki, Japan, and was awarded a D.Phil. (Computer Vision) from the University of Oxford in 1991.
Roberto’s research interests are in computer vision and robotics and include the recovery of motion and 3D shape of visible surfaces from image sequences; object detection and recognition; novel man-machine interfaces using hand, face and body gestures; real-time visual tracking for localisation and robot guidance; applications of computer vision in mobile phones, visual inspection and image-retrieval and video search.
Talk title: Making machines that see: Geometry, Uncertainty and Deep Learning
Synopsis: The last decade has seen a revolution in the theory and application of computer vision and machine learning. I will begin with a brief review of some of the fundamentals with a few examples of the reconstruction, registration and recognition of three-dimensional objects and their translation into novel commercial applications.
I will then introduce some recent results from real-time deep learning systems that exploit geometry and compute model uncertainty. Understanding what a model does not know is a critical part of safe machine learning systems. New tools, such as Bayesian deep learning, provide a framework for understanding uncertainty in deep learning models, aiding interpretability and safety of such systems. Additionally, knowledge of geometry is an important consideration in designing effective algorithms. In particular, we will explore the use of geometry to help design networks that can be trained with unlabelled data for stereo and for human body pose and shape recovery.
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