Bringing Traditional Computer Vision into Deep Learning
Mathieu Salzmann
(EPFL Lausannne, Switzerland)
Abstract:
In this talk, I will discuss our recent progress in developing algorithms that
incorporate traditional computer vision concepts into deep learning based
approaches. In particular, in the first part of the talk, I will introduce a new
type of deep architecture that exploits the notion of covariance descriptors
employed in the past for visual recognition. I will show that this architecture
can further be motivated from a statistical point of view. In the second part of
the talk, I will describe an approach to performing novel view synthesis that
exploits notions of geometry within a deep learning framework.