Kai Krajsek

34 papers A* 4A 1B 1Journal 8Unranked 18
YearRankTypeTitle / Venue / Authors
2026 A* conf
Self-Supervised Learning Based on Transformed Image Reconstruction for Equivariance-Coherent Feature Representation.
AAAI
Qin Wang, Alessio Quercia, Benjamin Bruns, Abigail Morrison, Hanno Scharr, Kai Krajsek
2025 J jnl
Engineering a large-scale data analytics and array computing library for research: Heat.
Electron. Commun. Eur. Assoc. Softw. Sci. Technol.
Fabian Hoppe, Juan Pedro Gutiérrez H. Muriedas, Michael Tarnawa, Philipp Knechtges, Björn Hagemeier, Kai Krajsek, Alexander Rüttgers, Markus Götz, Claudia Comito
2025 A conf
Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks.
WACV
Alessio Quercia, Erenus Yildiz, Zhuo Cao, Kai Krajsek, Abigail Morrison, Ira Assent, Hanno Scharr
2025 J jnl
Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks.
CoRR
Alessio Quercia, Erenus Yildiz, Zhuo Cao, Kai Krajsek, Abigail Morrison, Ira Assent, Hanno Scharr
2025 — conf
Rescuing Easy Samples in Self-Supervised Pretraining.
VISIGRAPP (2): VISAPP
Qin Wang, Kai Krajsek, Hanno Scharr
2025 J jnl
Self-Supervised Learning based on Transformed Image Reconstruction for Equivariance-Coherent Feature Representation.
CoRR
Qin Wang, Benjamin Bruns, Hanno Scharr, Kai Krajsek
2024 J jnl
Equivariant Representation Learning for Augmentation-based Self-Supervised Learning via Image Reconstruction.
CoRR
Qin Wang, Kai Krajsek, Hanno Scharr
2021 — conf
JUWELS Booster - A Supercomputer for Large-Scale AI Research.
ISC Workshops
Stefan Kesselheim, Andreas Herten, Kai Krajsek, Jan Ebert, Jenia Jitsev, Mehdi Cherti, Michael Langguth, Bing Gong, Scarlet Stadtler, Amirpasha Mozaffari, Gabriele Cavallaro, Rocco Sedona, Alexander Schug, Alexandre Strube, Roshni Kamath, Martin G. Schultz, Morris Riedel, Thomas Lippert
2021 J jnl
JUWELS Booster - A Supercomputer for Large-Scale AI Research.
CoRR
Stefan Kesselheim, Andreas Herten, Kai Krajsek, Jan Ebert, Jenia Jitsev, Mehdi Cherti, Michael Langguth, Bing Gong, Scarlet Stadtler, Amirpasha Mozaffari, Gabriele Cavallaro, Rocco Sedona, Alexander Schug, Alexandre Strube, Roshni Kamath, Martin G. Schultz, Morris Riedel, Thomas Lippert
2020 — conf
Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent.
LOD (2)
Alper Yegenoglu, Kai Krajsek, Sandra Díaz-Pier, Michael Herty
2020 — conf
HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics.
IEEE BigData
Markus Götz, Charlotte Debus, Daniel Coquelin, Kai Krajsek, Claudia Comito, Philipp Knechtges, Björn Hagemeier, Michael Tarnawa, Simon Hanselmann, Martin Siggel, Achim Basermann, Achim Streit
2020 J jnl
HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics.
CoRR
Markus Götz, Daniel Coquelin, Charlotte Debus, Kai Krajsek, Claudia Comito, Philipp Knechtges, Björn Hagemeier, Michael Tarnawa, Simon Hanselmann, Martin Siggel, Achim Basermann, Achim Streit
2016 J jnl
A Riemannian Bayesian Framework for Estimating Diffusion Tensor Images.
Int. J. Comput. Vis.
Kai Krajsek, Marion I. Menzel, Hanno Scharr
2016 — conf
An Online Algorithm for Efficient and Temporally Consistent Subspace Clustering.
ACCV (1)
Vasileios Zografos, Kai Krajsek, Bjoern H. Menze
2015 — —
Probabilistic models and estimation: theoretical concepts for variational approaches to computer vision.
Kai Krajsek
2014 — conf
Likelihood Functions for Errors-in-variables Models - Bias-free Local Estimation with Minimum Variance.
VISAPP (3)
Kai Krajsek, Christian Heinemann, Hanno Scharr
2014 — conf
Using Channel Representations in Regularization Terms - A Case Study on Image Diffusion.
VISAPP (1)
Christian Heinemann, Freddie Åström, George Baravdish, Kai Krajsek, Michael Felsberg, Hanno Scharr
2012 A* conf
A Riemannian approach for estimating orientation distribution function (ODF) images from high-angular resolution diffusion imaging (HARDI).
CVPR
Kai Krajsek, Hanno Scharr
2012 — ch.
A Short Introduction to Diffusion-Like Methods.
Mathematical Methods for Signal and Image Analysis and Representation
Hanno Scharr, Kai Krajsek
2011 — conf
An Estimation Theoretical Approach to Ambrosio-Tortorelli Image Segmentation.
DAGM-Symposium
Kai Krajsek, Ines Dedovic, Hanno Scharr
2010 A* conf
Diffusion filtering without parameter tuning: Models and inference tools.
CVPR
Kai Krajsek, Hanno Scharr
2009 A* conf
Riemannian Bayesian estimation of diffusion tensor images.
ICCV
Kai Krajsek, Marion I. Menzel, Hanno Scharr
2008 J jnl
Building Blocks for Computer Vision with Stochastic Partial Differential Equations.
Int. J. Comput. Vis.
Tobias Preusser, Hanno Scharr, Kai Krajsek, Robert M. Kirby
2008 — conf
Riemannian Anisotropic Diffusion for Tensor Valued Images.
ECCV (4)
Kai Krajsek, Marion I. Menzel, Michael Zwanger, Hanno Scharr
2008 — conf
Statistically Optimal Averaging for Image Restoration and Optical Flow Estimation.
DAGM-Symposium
Kai Krajsek, Rudolf Mester, Hanno Scharr
2007 — conf
Bayesian Model Selection for Optical Flow Estimation.
DAGM-Symposium
Kai Krajsek, Rudolf Mester
2007 — conf
Highly Accurate Orientation Estimation using Steerable Filters.
ICIP (5)
Jakub K. Kominiarczuk, Kai Krajsek, Rudolf Mester
2007 — conf
View-Based Robot Localization Using Spherical Harmonics: Concept and First Experimental Results.
DAGM-Symposium
Holger Friedrich, David Dederscheck, Kai Krajsek, Rudolf Mester
2006 B conf
A Maximum Likelihood Estimator for Choosing the Regularization Parameters in Global Optical Flow Methods.
ICIP
Kai Krajsek, Rudolf Mester
2006 — conf
A Unified Theory for Steerable and Quadrature Filters.
VISIGRAPP (Selected Papers)
Kai Krajsek, Rudolf Mester
2006 — conf
An unified theory for steerable and quadrature filters.
VISAPP (1)
Kai Krajsek, Rudolf Mester
2006 — conf
The Edge Preserving Wiener Filter for Scalar and Tensor Valued Images.
DAGM-Symposium
Kai Krajsek, Rudolf Mester
2005 — conf
Signal and Noise Adapted Filters for Differential Motion Estimation.
DAGM-Symposium
Kai Krajsek, Rudolf Mester
2004 — conf
Wiener-Optimized Discrete Filters for Differential Motion Estimation.
IWCM
Kai Krajsek, Rudolf Mester