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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
Structured Universal Adversarial Attacks on Object Detection for Video Sequences
Sven Jacob, Weijia Shao, Gjergji Kasneci
Video-based object detection plays a vital role in safety-critical applications. While deep learning-based object detectors have achieved impressive performance, they remain vulner…
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…