2 citations · 5 across the 5 of their papers we have counts for
5 papers
Streamlining the Development of Active Learning Methods in Real-World Object Detection
Moussa Kassem Sbeyti, Nadja Klein, Michelle Karg +2
Active learning (AL) for real-world object detection faces computational and reliability challenges that limit practical deployment. Developing new AL methods requires training mul…
Generative AI for Autonomous Driving: A Review
Katharina Winter, Abhishek Vivekanandan, Rupert Polley +17
Generative AI (GenAI) is rapidly advancing the field of Autonomous Driving (AD), extending beyond traditional applications in text, image, and video generation. We explore how gene…
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification
Kumar Manas, Christian Schlauch, Adrian Paschke +2
Deep learning-based trajectory prediction models have demonstrated promising capabilities in capturing complex interactions. However, their out-of-distribution generalization remai…
Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection
Moussa Kassem Sbeyti, Michelle Karg, Christian Wirth +2
Object detectors in real-world applications often fail to detect objects due to varying factors such as weather conditions and noisy input. Therefore, a process that mitigates fals…
Informed Spectral Normalized Gaussian Processes for Trajectory Prediction
Christian Schlauch, Christian Wirth, Nadja Klein
Prior parameter distributions provide an elegant way to represent prior expert and world knowledge for informed learning. Previous work has shown that using such informative priors…