Search Results for author:
Found 21 papers, 2 papers with code
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data
These maps indicate the importance of each 3D point in predicting the specific objects.
Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations
Conclusion: This suggests that pre-existing annotations can be an inexpensive resource in clinics to ease ill-posed and repetitive tasks like cross-section extraction for surveillance of aortic dissections.
Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild
We present a novel 3D pose refinement approach based on differentiable rendering for objects of arbitrary categories in the wild.
ALCN: Adaptive Local Contrast Normalization
We show that our method significantly outperforms standard normalization methods and would also be appear to be universal since it does not have to be re-trained for each new application.
Performing Arithmetic Using a Neural Network Trained on Digit Permutation Pairs
In this paper a neural network is trained to perform simple arithmetic using images of concatenated handwritten digit pairs.
Patch augmentation: Towards efficient decision boundaries for neural networks
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 attacks.
L*ReLU: Piece-wise Linear Activation Functions for Deep Fine-grained Visual Categorization
We demonstrate our approach for the task of Fine-grained Visual Categorization (FGVC), running experiments on seven different benchmark datasets.
Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation
We propose a novel method to efficiently estimate the spatial layout of a room from a single monocular RGB image.
Location Field Descriptors: Single Image 3D Model Retrieval in the Wild
We present Location Field Descriptors, a novel approach for single image 3D model retrieval in the wild.
GP2C: Geometric Projection Parameter Consensus for Joint 3D Pose and Focal Length Estimation in the Wild
We present a joint 3D pose and focal length estimation approach for object categories in the wild.
Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data
Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data.
The Quest for the Golden Activation Function
To avoid the manual design or selection of activation functions, we build on the idea of genetic algorithms to learn the best activation function for a given task.
3D Pose Estimation and 3D Model Retrieval for Objects in the Wild
We propose a scalable, efficient and accurate approach to retrieve 3D models for objects in the wild.
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology
The foundation of such an "augmented pathologist" needs an integrated approach: While machine learning algorithms require many thousands of training examples, a human expert is often confronted with only a few data points.
ALCN: Meta-Learning for Contrast Normalization Applied to Robust 3D Pose Estimation
We therefore propose a novel illumination normalization method that lets us learn to detect objects and estimate their 3D pose under challenging illumination conditions from very few training samples.
Learning to Align Semantic Segmentation and 2.5D Maps for Geolocalization
We present an efficient method for geolocalization in urban environments starting from a coarse estimate of the location provided by a GPS and using a simple untextured 2. 5D model of the surrounding buildings.
Accurate Object Detection with Joint Classification-Regression Random Forests
In this way, we can simultaneously predict the object probability of a window in a sliding window approach as well as regress its aspect ratio with a single model.
Occlusion Geodesics for Online Multi-Object Tracking
Robust multi-object tracking-by-detection requires the correct assignment of noisy detection results to object trajectories.
Optimizing 1-Nearest Prototype Classifiers
The development of complex, powerful classifiers and their constant improvement have contributed much to the progress in many fields of computer vision.
Robust Real-Time Tracking of Multiple Objects by Volumetric Mass Densities
Combining foreground images from multiple views by projecting them onto a common ground-plane has been recently applied within many multi-object tracking approaches.
Alternating Decision Forests
Contrary to Boosted Trees, in our method the loss minimization is an inherent part of the tree growing process, thus allowing to keep the benefits of common Random Forests, such as, parallel processing.
