3 papers
cs.CV2026
Adversarial Attack and Disturbance Detection by Hadamard-Coded Output Representations for Object Detection and Semantic Segmentation
Lucas Görnhardt, Timo Bartels, Niklas Schwarz +1
Conventional one-hot encodings often yield poorly calibrated models, being overconfident under attack, and letting entropy-based detection algorithms fail. Previous image classific…
cs.CV2026
Efficient Multi-View 3D Object Detection by Dynamic Token Selection and Fine-Tuning
Danish Nazir, Antoine Hanna-Asaad, Lucas Görnhardt +3
Existing multi-view three-dimensional (3D) object detection approaches widely adopt large-scale pre-trained vision transformer (ViT)-based foundation models as backbones, being com…
eess.IV2025
A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images Only
Björn Möller, Lucas Görnhardt, Tim Fingscheidt
Transformer architectures prominently lead single-image super-resolution (SISR) benchmarks, reconstructing high-resolution (HR) images from their low-resolution (LR) counterparts.…