1 citations · 1 across the 6 of their papers we have counts for
6 papers
SSFT: A Lightweight Spectral-Spatial Fusion Transformer for Generic Hyperspectral Classification
Alexander Musiat, Nikolas Ebert, Oliver Wasenmüller
Hyperspectral imaging enables fine-grained recognition of materials by capturing rich spectral signatures, but learning robust classifiers is challenging due to high dimensionality…
Spatial self-supervised Peak Learning and correlation-based Evaluation of peak picking in Mass Spectrometry Imaging
Philipp Weigand, Nikolas Ebert, Shad A. Mohammed +3
Mass spectrometry imaging (MSI) enables label-free visualization of molecular distributions across tissue samples but generates large and complex datasets that require effective pe…
D-PLS: Decoupled Semantic Segmentation for 4D-Panoptic-LiDAR-Segmentation
Maik Steinhauser, Laurenz Reichardt, Nikolas Ebert +1
This paper introduces a novel approach to 4D Panoptic LiDAR Segmentation that decouples semantic and instance segmentation, leveraging single-scan semantic predictions as prior inf…
Boosting Few-Shot Detection with Large Language Models and Layout-to-Image Synthesis
Ahmed Abdullah, Nikolas Ebert, Oliver Wasenmüller
Recent advancements in diffusion models have enabled a wide range of works exploiting their ability to generate high-volume, high-quality data for use in various downstream tasks.…
Text3DAug -- Prompted Instance Augmentation for LiDAR Perception
Laurenz Reichardt, Luca Uhr, Oliver Wasenmüller
LiDAR data of urban scenarios poses unique challenges, such as heterogeneous characteristics and inherent class imbalance. Therefore, large-scale datasets are necessary to apply de…
GenFormer -- Generated Images are All You Need to Improve Robustness of Transformers on Small Datasets
Sven Oehri, Nikolas Ebert, Ahmed Abdullah +2
Recent studies showcase the competitive accuracy of Vision Transformers (ViTs) in relation to Convolutional Neural Networks (CNNs), along with their remarkable robustness. However,…