16 citations · 19 across the 7 of their papers we have counts for
10 papers
Next-Best-View Estimation based on Deep Reinforcement Learning for Active Object Classification
Christian Korbach, Markus D. Solbach, Raphael Memmesheimer +2
The presentation and analysis of image data from a single viewpoint are often not sufficient to solve a task. Several viewpoints are necessary to obtain more information. The next-…
Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks
Michael Duhme, Raphael Memmesheimer, Dietrich Paulus
In this paper, we present Fusion-GCN, an approach for multimodal action recognition using Graph Convolutional Networks (GCNs). Action recognition methods based around GCNs recently…
Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action Recognition
Raphael Memmesheimer, Simon Häring, Nick Theisen +1
One-shot action recognition allows the recognition of human-performed actions with only a single training example. This can influence human-robot-interaction positively by enabling…
Gimme Signals: Discriminative signal encoding for multimodal activity recognition
Raphael Memmesheimer, Nick Theisen, Dietrich Paulus
We present a simple, yet effective and flexible method for action recognition supporting multiple sensor modalities. Multivariate signal sequences are encoded in an image and are t…
SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition
Raphael Memmesheimer, Nick Theisen, Dietrich Paulus
Recognizing an activity with a single reference sample using metric learning approaches is a promising research field. The majority of few-shot methods focus on object recognition…
Gesture Recognition in RGB Videos UsingHuman Body Keypoints and Dynamic Time Warping
Pascal Schneider, Raphael Memmesheimer, Ivanna Kramer +1
Gesture recognition opens up new ways for humans to intuitively interact with machines. Especially for service robots, gestures can be a valuable addition to the means of communica…