84 citations · 392 across the 55 of their papers we have counts for
6 papers · 1 filter
Neural Random Forest Imitation
Christoph Reinders, Bodo Rosenhahn
We present Neural Random Forest Imitation - a novel approach for transforming random forests into neural networks. Existing methods propose a direct mapping and produce very ineffi…
Learning Disentangled Representations via Independent Subspaces
Maren Awiszus, Hanno Ackermann, Bodo Rosenhahn
Image generating neural networks are mostly viewed as black boxes, where any change in the input can have a number of globally effective changes on the output. In this work, we pro…
Structuring Autoencoders
Marco Rudolph, Bastian Wandt, Bodo Rosenhahn
In this paper we propose Structuring AutoEncoders (SAE). SAEs are neural networks which learn a low dimensional representation of data which are additionally enriched with a desire…
Temporally Consistent Horizon Lines
Florian Kluger, Hanno Ackermann, Michael Ying Yang +1
The horizon line is an important geometric feature for many image processing and scene understanding tasks in computer vision. For instance, in navigation of autonomous vehicles or…
Target-Tailored Source-Transformation for Scene Graph Generation
Wentong Liao, Cuiling Lan, Wenjun Zeng +2
Scene graph generation aims to provide a semantic and structural description of an image, denoting the objects (with nodes) and their relationships (with edges). The best performin…
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation
Bastian Wandt, Bodo Rosenhahn
This paper addresses the problem of 3D human pose estimation from single images. While for a long time human skeletons were parameterized and fitted to the observation by satisfyin…