Zobrazeno 1 - 10
of 357
pro vyhledávání: '"A Helminger"'
Autor:
Bühler, Marcel C., Li, Gengyan, Wood, Erroll, Helminger, Leonhard, Chen, Xu, Shah, Tanmay, Wang, Daoye, Garbin, Stephan, Orts-Escolano, Sergio, Hilliges, Otmar, Lagun, Dmitry, Riviere, Jérémy, Gotardo, Paulo, Beeler, Thabo, Meka, Abhimitra, Sarkar, Kripasindhu
Volumetric modeling and neural radiance field representations have revolutionized 3D face capture and photorealistic novel view synthesis. However, these methods often require hundreds of multi-view input images and are thus inapplicable to cases wit
Externí odkaz:
http://arxiv.org/abs/2410.00630
Autor:
Bühler, Marcel C., Sarkar, Kripasindhu, Shah, Tanmay, Li, Gengyan, Wang, Daoye, Helminger, Leonhard, Orts-Escolano, Sergio, Lagun, Dmitry, Hilliges, Otmar, Beeler, Thabo, Meka, Abhimitra
NeRFs have enabled highly realistic synthesis of human faces including complex appearance and reflectance effects of hair and skin. These methods typically require a large number of multi-view input images, making the process hardware intensive and c
Externí odkaz:
http://arxiv.org/abs/2309.16859
Autor:
Walch, Roman, Sousa, Samuel, Helminger, Lukas, Lindstaedt, Stefanie, Rechberger, Christian, Trügler, Andreas
Big data has been a pervasive catchphrase in recent years, but dealing with data scarcity has become a crucial question for many real-world deep learning (DL) applications. A popular methodology to efficiently enable the training of DL models to perf
Externí odkaz:
http://arxiv.org/abs/2205.11935
Autor:
Helminger, Leonhard, Azevedo, Roberto, Djelouah, Abdelaziz, Gross, Markus, Schroers, Christopher
Recently, significant progress has been made in learned image and video compression. In particular the usage of Generative Adversarial Networks has lead to impressive results in the low bit rate regime. However, the model size remains an important is
Externí odkaz:
http://arxiv.org/abs/2201.02624
Autor:
Helminger, Leonhard, Bernasconi, Michael, Djelouah, Abdelaziz, Gross, Markus, Schroers, Christopher
Image restoration has seen great progress in the last years thanks to the advances in deep neural networks. Most of these existing techniques are trained using full supervision with suitable image pairs to tackle a specific degradation. However, in a
Externí odkaz:
http://arxiv.org/abs/2009.04583
Deep learning based image compression has recently witnessed exciting progress and in some cases even managed to surpass transform coding based approaches that have been established and refined over many decades. However, state-of-the-art solutions f
Externí odkaz:
http://arxiv.org/abs/2008.10486
Autor:
Bampoulidis, Alexandros, Bruni, Alessandro, Helminger, Lukas, Kales, Daniel, Rechberger, Christian, Walch, Roman
Recent work has shown that cell phone mobility data has the unique potential to create accurate models for human mobility and consequently the spread of infected diseases. While prior studies have exclusively relied on a mobile network operator's sub
Externí odkaz:
http://arxiv.org/abs/2005.02061
Akademický článek
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Autor:
Kristin Ludewig, Yves P. Klinger, Tobias W. Donath, Lukas Bärmann, Carsten Eichberg, Jacob Gadegaad Thomsen, Eugen Görzen, Wiebke Hansen, Eliza M. Hasselquist, Thierry Helminger, Frida Kaiskog, Emma Karlsson, Torsten Kirchner, Carola Knudsen, Nikola Lenzewski, Sigrid Lindmo, Per Milberg, Daniel Pruchniewicz, Elisabeth Richter, Tobias M. Sandner, Judith M. Sarneel, Ralf Schmiede, Simone Schneider, Kathrin Schwarz, Åsa Tjäder, Barbara Tokarska-Guzik, Claudia Walczak, Odile Weber, Ludwik Żołnierz, Rolf Lutz Eckstein
Publikováno v:
NeoBiota, Vol 78, Iss , Pp 185-206 (2022)
Plant phenology, i. e. the timing of life cycle events, is related to individual fitness and species distribution ranges. Temperature is one of the most important drivers of plant phenology together with day length. The adaptation of their phenology
Externí odkaz:
https://doaj.org/article/61e24adc0f0b47d3a7f0b7df19b8f679
We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input. Our approach exploits regularities in sequential data that exist regardless of the order in which the data is viewed. The res
Externí odkaz:
http://arxiv.org/abs/1812.03962