70 citations · 101 across the 9 of their papers we have counts for
11 papers
Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild
Alexander Grabner, Yaming Wang, Peizhao Zhang +5
We present a novel 3D pose refinement approach based on differentiable rendering for objects of arbitrary categories in the wild. In contrast to previous methods, we make two main…
ALCN: Adaptive Local Contrast Normalization
Mahdi Rad, Peter M. Roth, Vincent Lepetit
To make Robotics and Augmented Reality applications robust to illumination changes, the current trend is to train a Deep Network with training images captured under many different…
Performing Arithmetic Using a Neural Network Trained on Digit Permutation Pairs
Marcus D. Bloice, Peter M. Roth, Andreas Holzinger
In this paper a neural network is trained to perform simple arithmetic using images of concatenated handwritten digit pairs. A convolutional neural network was trained with images…
Patch augmentation: Towards efficient decision boundaries for neural networks
Marcus D. Bloice, Peter M. Roth, Andreas Holzinger
In this paper we propose a new augmentation technique, called patch augmentation, that, in our experiments, improves model accuracy and makes networks more robust to adversarial at…
L*ReLU: Piece-wise Linear Activation Functions for Deep Fine-grained Visual Categorization
Mina Basirat, Peter M. Roth
Deep neural networks paved the way for significant improvements in image visual categorization during the last years. However, even though the tasks are highly varying, differing i…
Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation
Martin Hirzer, Peter M. Roth, Vincent Lepetit
We propose a novel method to efficiently estimate the spatial layout of a room from a single monocular RGB image. As existing approaches based on low-level feature extraction, foll…