43 citations · 116 across the 19 of their papers we have counts for
10 papers · 1 filter
Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection
Nishant Kumar, Siniša Šegvić, Abouzar Eslami +1
Real-world deployment of reliable object detectors is crucial for applications such as autonomous driving. However, general-purpose object detectors like Faster R-CNN are prone to…
Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration
David Bojanić, Kristijan Bartol, Josep Forest +3
Learning universal representations across different applications domain is an open research problem. In fact, finding universal architecture within the same application but across…
Enhancing Fairness of Visual Attribute Predictors
Tobias Hänel, Nishant Kumar, Dmitrij Schlesinger +4
The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes. We…
FuseVis: Interpreting neural networks for image fusion using per-pixel saliency visualization
Nishant Kumar, Stefan Gumhold
Image fusion helps in merging two or more images to construct a more informative single fused image. Recently, unsupervised learning based convolutional neural networks (CNN) have…
Visualisation of Medical Image Fusion and Translation for Accurate Diagnosis of High Grade Gliomas
Nishant Kumar, Nico Hoffmann, Matthias Kirsch +1
The medical image fusion combines two or more modalities into a single view while medical image translation synthesizes new images and assists in data augmentation. Together, these…
Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task
Aritra Bhowmik, Stefan Gumhold, Carsten Rother +1
We address a core problem of computer vision: Detection and description of 2D feature points for image matching. For a long time, hand-crafted designs, like the seminal SIFT algori…
Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift
Titus Leistner, Hendrik Schilling, Radek Mackowiak +2
We propose a method for depth estimation from light field data, based on a fully convolutional neural network architecture. Our goal is to design a pipeline which achieves highly a…
Global Hypothesis Generation for 6D Object Pose Estimation
Frank Michel, Alexander Kirillov, Eric Brachmann +4
This paper addresses the task of estimating the 6D pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) Compute local feat…
DSAC - Differentiable RANSAC for Camera Localization
Eric Brachmann, Alexander Krull, Sebastian Nowozin +4
RANSAC is an important algorithm in robust optimization and a central building block for many computer vision applications. In recent years, traditionally hand-crafted pipelines ha…
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
Alexander Krull, Eric Brachmann, Frank Michel +3
Analysis-by-synthesis has been a successful approach for many tasks in computer vision, such as 6D pose estimation of an object in an RGB-D image which is the topic of this work. T…