Learning to see and act in dynamic three-dimensional environments (Talk)
Our world is dynamic and three-dimensional. Understanding the 3D layout of scenes and the motion of objects is crucial for successfully operating in such an environment. I will talk about two lines of recent research in this direction. One is on end-to-end learning of motion and 3D structure: optical flow estimation, binocular and monocular stereo, direct generation of large volumes with convolutional networks. The other is on sensorimotor control in immersive three-dimensional environments, learned from experience or from demonstration.
Biography: Alexey Dosovitskiy received his Specialist (equivalent of MSc) and Ph.D. degrees in mathematics (functional analysis) from Moscow State University in 2009 and 2012 respectively. He spent 2013-2016 as a postdoctoral researcher at the Computer Vision Group of Prof. Thomas Brox at the University of Freiburg in Germany, with research focus on deep learning, specifically unsupervised learning, image generation with neural networks, motion and 3D structure estimation. Since January 2017 Alexey works on deep learning and sensorimotor control at Intel Visual Computing Lab led by Dr. Vladlen Koltun.
