41 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of KaiserslauternDE54 papers
- German Research Centre for Artificial IntelligenceDE7 papers
- Fraunhofer Institute for Industrial MathematicsDE6 papers
- Rheinland-Pfälzische Technische Universität Kaiserslautern-LandauDE5 papers
- Institut national de recherche en sciences et technologies du numériqueFR3 papers
- Johannes Gutenberg University MainzDE3 papers
- Karlsruhe Institute of TechnologyDE3 papers
- University of Koblenz and LandauDE3 papers
- Baden-Wuerttemberg Cooperative State UniversityDE2 papers
- Eindhoven University of TechnologyNL2 papers
- Georgia Institute of TechnologyUS2 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di PadovaIT2 papers
7 papers · 1 filter
Unsupervised Learning for Industrial Defect Detection: A Case Study on Shearographic Data
Jessica Plassmann, Nicolas Schuler, Georg von Freymann +1
Shearography is a non-destructive testing method for detecting subsurface defects, offering high sensitivity and full-field inspection capabilities. However, its industrial adoptio…
Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration
Francisco Mena, Dino Ienco, Cassio F. Dantas +2
Multi-modal co-learning is emerging as an effective paradigm in machine learning, enabling models to collaboratively learn from different modalities to enhance single-modality pred…
XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation
Raju Ningappa Mulawade, Christoph Garth, Alexander Wiebel
We propose a novel segmentation-based explainable artificial intelligence (XAI) method for neural networks working on point cloud classification. As one building block of this meth…
Cracks in concrete
Tin Barisin, Christian Jung, Anna Nowacka +2
Finding and properly segmenting cracks in images of concrete is a challenging task. Cracks are thin and rough and being air filled do yield a very weak contrast in 3D images obtain…
A Comparative Assessment of Multi-view fusion learning for Crop Classification
Francisco Mena, Diego Arenas, Marlon Nuske +1
With a rapidly increasing amount and diversity of remote sensing (RS) data sources, there is a strong need for multi-view learning modeling. This is a complex task when considering…
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs
Vladimir Rybalkin, Alessandro Pappalardo, Muhammad Mohsin Ghaffar +3
It is well known that many types of artificial neural networks, including recurrent networks, can achieve a high classification accuracy even with low-precision weights and activat…
Fast Feature Extraction with CNNs with Pooling Layers
Christian Bailer, Tewodros Habtegebrial, Kiran varanasi +1
In recent years, many publications showed that convolutional neural network based features can have a superior performance to engineered features. However, not much effort was take…