11 citations · 13 across the 5 of their papers we have counts for
7 papers · 1 filter
Cross-Domain Transfer of Hyperspectral Foundation Models
Nick Theisen, Peer Neubert
Hyperspectral imaging (HSI) semantic segmentation typically relies on in-domain training, but limited data availability often restricts model performance in real-world applications…
Data-Efficient Spectral Classification of Hyperspectral Data Using MiniROCKET and HDC-MiniROCKET
Nick Theisen, Kenny Schlegel, Dietrich Paulus +1
The classification of pixel spectra of hyperspectral images, i.e. spectral classification, is used in many fields ranging from agricultural, over medical to remote sensing applicat…
HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios
Nick Theisen, Robin Bartsch, Dietrich Paulus +1
Semantic segmentation is an essential step for many vision applications in order to understand a scene and the objects within. Recent progress in hyperspectral imaging technology e…
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…