1 code implementation • 2 Dec 2019 • Fabian Groh, Lukas Ruppert, Patrick Wieschollek, Hendrik P. A. Lensch
Approximate nearest neighbor (ANN) search in high dimensions is an integral part of several computer vision systems and gains importance in deep learning with explicit memory representations.
1 code implementation • 20 Mar 2018 • Fabian Groh, Patrick Wieschollek, Hendrik P. A. Lensch
Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid.
no code implementations • ECCV 2018 • Patrick Wieschollek, Orazio Gallo, Jinwei Gu, Jan Kautz
The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms.
1 code implementation • ICCV 2017 • Patrick Wieschollek, Michael Hirsch, Bernhard Schölkopf, Hendrik P. A. Lensch
As handheld video cameras are now commonplace and available in every smartphone, images and videos can be recorded almost everywhere at anytime.
1 code implementation • CVPR 2016 • Patrick Wieschollek, Oliver Wang, Alexander Sorkine-Hornung, Hendrik P. A. Lensch
We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization.
no code implementations • 8 Feb 2017 • Patrick Wieschollek, Fabian Groh, Hendrik P. A. Lensch
Fisher-Vectors (FV) encode higher-order statistics of a set of multiple local descriptors like SIFT features.
2 code implementations • 16 Nov 2016 • Katharina Schwarz, Patrick Wieschollek, Hendrik P. A. Lensch
Rating how aesthetically pleasing an image appears is a highly complex matter and depends on a large number of different visual factors.
no code implementations • 19 Oct 2016 • Patrick Wieschollek, Ido Freeman, Hendrik P. A. Lensch
Aligning video sequences is a fundamental yet still unsolved component for a broad range of applications in computer graphics and vision.
no code implementations • 20 Sep 2016 • Patrick Wieschollek, Hendrik P. A. Lensch
Specifically, transfer learning from the task of object recognition is exploited to more effectively train good features for material classification.
no code implementations • 15 Jul 2016 • Patrick Wieschollek, Bernhard Schölkopf, Hendrik P. A. Lensch, Michael Hirsch
We present a neural network model approach for multi-frame blind deconvolution.