25 citations · 40 across the 8 of their papers we have counts for
8 papers
Learning Dense Visual Descriptors using Image Augmentations for Robot Manipulation Tasks
Christian Graf, David B. Adrian, Joshua Weil +5
We propose a self-supervised training approach for learning view-invariant dense visual descriptors using image augmentations. Unlike existing works, which often require complex da…
Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression
Felix Ott, Nisha Lakshmana Raichur, David Rügamer +4
Visual-inertial localization is a key problem in computer vision and robotics applications such as virtual reality, self-driving cars, and aerial vehicles. The goal is to estimate…
Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation
David B. Adrian, Andras Gabor Kupcsik, Markus Spies +1
We propose a framework for robust and efficient training of Dense Object Nets (DON) with a focus on multi-object robot manipulation scenarios. DON is a popular approach to obtain d…
Defining Gaze Patterns for Process Model Literacy -- Exploring Visual Routines in Process Models with Diverse Mappings
Michael Winter, Heiko Neumann, Rüdiger Pryss +2
Process models depict crucial artifacts for organizations regarding documentation, communication, and collaboration. The proper comprehension of such models is essential for an eff…
An Empirical Study towards Understanding How Deep Convolutional Nets Recognize Falls
Yan Zhang, Heiko Neumann
Detecting unintended falls is essential for ambient intelligence and healthcare of elderly people living alone. In recent years, deep convolutional nets are widely used in human ac…
Local Temporal Bilinear Pooling for Fine-grained Action Parsing
Yan Zhang, Siyu Tang, Krikamol Muandet +2
Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle a…
Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks
Alex Bäuerle, Heiko Neumann, Timo Ropinski
Training data plays an essential role in modern applications of machine learning. However, gathering labeled training data is time-consuming. Therefore, labeling is often outsource…
Temporal Human Action Segmentation via Dynamic Clustering
Yan Zhang, He Sun, Siyu Tang +1
We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and…