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pro vyhledávání: '"Cotter, Fergal"'
Autor:
Zürn, Jannik, Gladkov, Paul, Dudas, Sofía, Cotter, Fergal, Toteva, Sofi, Shotton, Jamie, Simaiaki, Vasiliki, Mohan, Nikhil
We present WayveScenes101, a dataset designed to help the community advance the state of the art in novel view synthesis that focuses on challenging driving scenes containing many dynamic and deformable elements with changing geometry and texture. Th
Externí odkaz:
http://arxiv.org/abs/2407.08280
We present a novel deep learning architecture for probabilistic future prediction from video. We predict the future semantics, geometry and motion of complex real-world urban scenes and use this representation to control an autonomous vehicle. This w
Externí odkaz:
http://arxiv.org/abs/2003.06409
Autor:
Cotter, Fergal, Kingsbury, Nick
In this paper we explore tying together the ideas from Scattering Transforms and Convolutional Neural Networks (CNN) for Image Analysis by proposing a learnable ScatterNet. Previous attempts at tying them together in hybrid networks have tended to ke
Externí odkaz:
http://arxiv.org/abs/1903.03137
Autor:
Dubourg-Felonneau, Geoffroy, Cannings, Timothy, Cotter, Fergal, Thompson, Hannah, Patel, Nirmesh, Cassidy, John W, Clifford, Harry W
The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings. However, the unique characteristics of these data are not always well suited to machine learning technique
Externí odkaz:
http://arxiv.org/abs/1811.10455
Autor:
Cotter, Fergal, Kingsbury, Nick
This paper examines the possibility of, and the possible advantages to learning the filters of convolutional neural networks (CNNs) for image analysis in the wavelet domain. We are stimulated by both Mallat's scattering transform and the idea of filt
Externí odkaz:
http://arxiv.org/abs/1811.06115
Autor:
Cotter, Fergal, Kingsbury, Nick
Scattering Transforms (or ScatterNets) introduced by Mallat are a promising start into creating a well-defined feature extractor to use for pattern recognition and image classification tasks. They are of particular interest due to their architectural
Externí odkaz:
http://arxiv.org/abs/1709.01355
Autor:
Cotter, Fergal
Image understanding has long been a goal for computer vision. It has proved to be an exceptionally difficult task due to the large amounts of variability that are inherent to objects in a scene. Recent advances in supervised learning methods, particu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a8dd3b710f51189a0027a80fffa53c8