Machine Learning and Computer Vision
DESCRIPTION
Computer vision models based on deep neural networks have reached impressive levels of performance in the past decade or so. We focus on how to effectively exploit large scale unlabeled data in order to improve the quality of learned visual data representations and to make these representations generalize to a much wider variety of tasks. We also develop novel deep learning-based methods for continual/incremental visual learning, where training data as well as visual tasks are presented to us in a sequential manner over time. Finally, we explore neuromorphic algorithms for visual motion perception and develop the foundations of multimodal machine learning and geometric deep learning as well as the intersection of tensor methods and deep learning with a focus on higher-order deep learning on multimodal/multiway data.
RESEARCHERS
Alexandros Dimakis
UT Austin
Kostas Daniilidis
UNIVERSITY OF PENNSYLVANIA
Nikos Komodakis
University of Crete
Sotirios Tsaftaris
THE UNIVERSITY OF EDINBURGH
Stefanos Nikolaidis
University of Southern California
Stefanos Zafeiriou
Imperial College London
