16 citations · 20 across the 16 of their papers we have counts for
9 papers · 1 filter
Efficient Image Annotation via Semi-Supervised Object Segmentation with Label Propagation
Vitalii Tutevych, Raphael Memmesheimer, Luca Eichler +4
Reliable object perception is necessary for general-purpose service robots. Open-vocabulary detectors struggle to generalize beyond a few classes and fully supervised training of o…
LIAM: Multimodal Transformer for Language Instructions, Images, Actions and Semantic Maps
Yihao Wang, Raphael Memmesheimer, Sven Behnke
The availability of large language models and open-vocabulary object perception methods enables more flexibility for domestic service robots. The large variability of domestic task…
Person Segmentation and Action Classification for Multi-Channel Hemisphere Field of View LiDAR Sensors
Svetlana Seliunina, Artem Otelepko, Raphael Memmesheimer +1
Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that…
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…