3 papers
cs.CV2025
Stylized Synthetic Augmentation further improves Corruption Robustness
Georg Siedel, Rojan Regmi, Abhirami Anand +3
This paper proposes a training data augmentation pipeline that combines synthetic image data with neural style transfer in order to address the vulnerability of deep vision models…
cs.CV2025
Combined Image Data Augmentations diminish the benefits of Adaptive Label Smoothing
Georg Siedel, Ekagra Gupta, Weijia Shao +2
Soft augmentation regularizes the supervised learning process of image classifiers by reducing label confidence of a training sample based on the magnitude of random-crop augmentat…
cs.RO2025
Dynamic Risk Assessment for Human-Robot Collaboration Using a Heuristics-based Approach
Georgios Katranis, Frederik Plahl, Joachim Grimstadt +3
Human-robot collaboration (HRC) introduces significant safety challenges, particularly in protecting human operators working alongside collaborative robots (cobots). While current…