Abstract In recent years, Discriminative Correlation Filter (DCF) based methods have
significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever …

Privacy-preserving multi-party machine learning allows multiple organizations to perform
collaborative data analytics while guaranteeing the privacy of their individual datasets …

We study the problem of generating plausible interpretations of a scene from a collection of
line segments automatically extracted from a single indoor image. We show that we can …

The state of the art in articulated hand tracking has been greatly advanced by hybrid
methods that fit a generative hand model to depth data, leveraging both temporally and …

Separable nonlinear models are very common in various research fields, such as machine
learning and system identification. The variable projection (VP) approach is efficient for the …

Matrix factorization (MF) is a fundamental model in data mining and machine learning, which
finds wide applications in diverse application areas, including recommendation systems with …

This paper investigates algorithms for matrix factorization when some or many components
are missing, a problem that arises frequently in computer vision and pattern recognition. We …

Current Internet transport protocols are being revisited in an attempt to respond to traffic
growth in size, diversity and the emergence of new applications. Designed to reduce Web …

Bundle adjustment is a key component in practically all available Structure from Motion
systems. While it is crucial for achieving accurate reconstruction, convergence to the right …

Variable Projection (VarPro) is a framework to solve optimization problems efficiently by
optimally eliminating a subset of the unknowns. It is in particular adapted for Separable …