no code implementations • 18 Jul 2023 • Dustin Aganian, Mona Köhler, Benedict Stephan, Markus Eisenbach, Horst-Michael Gross
As collaborative robots (cobots) continue to gain popularity in industrial manufacturing, effective human-robot collaboration becomes crucial.
no code implementations • 9 Jun 2023 • Dustin Aganian, Mona Köhler, Sebastian Baake, Markus Eisenbach, Horst-Michael Gross
Our research sheds light on the benefits of combining skeleton joints with object information for human action recognition in assembly tasks.
no code implementations • 17 Apr 2023 • Dustin Aganian, Benedict Stephan, Markus Eisenbach, Corinna Stretz, Horst-Michael Gross
With the emergence of collaborative robots (cobots), human-robot collaboration in industrial manufacturing is coming into focus.
no code implementations • 28 Feb 2023 • Markus Eisenbach, Jannik Lübberstedt, Dustin Aganian, Horst-Michael Gross
Person re-identification plays a key role in applications where a mobile robot needs to track its users over a long period of time, even if they are partially unobserved for some time, in order to follow them or be available on demand.
no code implementations • 22 Dec 2021 • Mona Köhler, Markus Eisenbach, Horst-Michael Gross
To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection aims to learn from few object instances of new categories in the target domain.
no code implementations • 23 Nov 2020 • Rick Archibald, Edmond Chow, Eduardo D'Azevedo, Jack Dongarra, Markus Eisenbach, Rocco Febbo, Florent Lopez, Daniel Nichols, Stanimire Tomov, Kwai Wong, Junqi Yin
This paper discusses the necessities of an HPC deep learning framework and how those needs can be provided (e. g., as in MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP.
no code implementations • 7 Sep 2019 • Massimiliano Lupo Pasini, Junqi Yin, Ying Wai Li, Markus Eisenbach
We propose a new scalable method to optimize the architecture of an artificial neural network.
no code implementations • 10 Aug 2019 • Jiaxin Zhang, Xianglin Liu, Sirui Bi, Junqi Yin, Guannan Zhang, Markus Eisenbach
In this study, a robust data-driven framework based on Bayesian approaches is proposed and demonstrated on the accurate and efficient prediction of configurational energy of high entropy alloys.