ERC Consolidator Grant · 2017
Deep Learning for Dynamic 3D Visual Scene Understanding
Over the past 5 years, deep learning has exercised a tremendous and transformational effect on the field of computer vision. However, deep neural networks (DNNs) can only realize their full potential when applied in an end-to-end manner, i.e., when every stage of the processing pipeline is differentiable with respect to the network’s parameters, such that all of those parameters can be optimized together. Such end-to-end learning solutions are still rare for computer vision problems, in particular for dynamic visual scene understanding tasks. Moreover, feed-forward processing, as done in most DNN-based vision approaches, is only a tiny fraction of what the human brain can do. Feedback…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.