Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare
in a geometrically faithful way point clouds and more generally probability distributions. The …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

Abstract The problem of Multiple Object Tracking (MOT) consists in following the trajectory of
different objects in a sequence, usually a video. In recent years, with the rise of Deep …

We consider the task of 3D pose estimation and trackingof multiple people seen in an
arbitrary number of camerafeeds. We propose TesseTrack, a novel top-down approachthat …

We review methods designed to compute correspondences between geometric shapes
represented by triangle meshes, contours or point sets. This survey is motivated in part by …

Accurate and reliable wind speed forecasting is important for the dispatch and management
of wind power generation systems. However, existing forecasting models based on the data …

We introduce a computationally efficient algorithm for multi-object tracking by detection that
addresses four main challenges: appearance similarity among targets, missing data due to …

We present the application of a deep machine learning technique to classify radio images of
extended sources on a morphological basis using convolutional neural networks (CNN). In …

We consider the problem of finding a matching between two sets of features, given complex
relations among them, going beyond pairwise. Each feature set is modeled by a hypergraph …

Data association is a key step within the multi-object tracking pipeline that is notoriously
challenging due to its combinatorial nature. A popular and general way to formulate data …