7 papers
TAP into the Patch Tokens: Leveraging Vision Foundation Model Features for AI-Generated Image Detection
Ahmed Abdullah, Nikolas Ebert, Oliver Wasenmüller
Recent methods demonstrate that large-scale pretrained models, such as CLIP vision transformers, effectively detect AI-generated images (AIGIs) from unseen generative models when u…
PointTransformerX: Portable and Efficient 3D Point Cloud Processing without Sparse Algorithms
Laurenz Reichardt, Nikolas Ebert, Oliver Wasenmüller
3D point cloud perception remains tightly coupled to custom CUDA operators for spatial operations, limiting portability and efficiency on non-NVIDIA, AMD, and embedded hardware. We…
IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging
Philipp Weigand, Niels Nawrot, Nikolas Ebert +2
Peak picking is a fundamental preprocessing step in Mass Spectrometry Imaging (MSI), where each sample is represented by hundreds to thousands of ion images. Existing approaches re…
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…