no code implementations • 8 Mar 2024 • Royden Wagner, Ömer Şahin Taş, Marvin Klemp, Carlos Fernandez
We present JointMotion, a self-supervised learning method for joint motion prediction in autonomous driving.
2 code implementations • 19 Jun 2023 • Royden Wagner, Omer Sahin Tas, Marvin Klemp, Carlos Fernandez Lopez
Predicting the future motion of traffic agents is vital for self-driving vehicles to ensure their safe operation.
1 code implementation • 12 Jun 2023 • Royden Wagner, Marvin Klemp, Carlos Fernandez Lopez
In self-driving applications, LiDAR data provides accurate information about distances in 3D but lacks the semantic richness of camera data.
no code implementations • 17 Feb 2023 • Marvin Klemp, Kevin Rösch, Royden Wagner, Jannik Quehl, Martin Lauer
Therefore, datasets used to train perception models of ITS must contain a significant number of vulnerable road users.
2 code implementations • 12 Feb 2023 • Royden Wagner, Carlos Fernandez Lopez, Christoph Stiller
Self-supervised learning, which is strikingly referred to as the dark matter of intelligence, is gaining more attention in biomedical applications of deep learning.
1 code implementation • 1 Jun 2022 • Royden Wagner, Karl Rohr
We propose a novel hybrid CNN-ViT model for cell detection in microscopy images to exploit the advantages of both types of deep learning models.
1 code implementation • 6 Apr 2022 • Royden Wagner, Karl Rohr
Since the number of available 3D microscopy images is typically limited in applications, we take a different approach and introduce a small CNN for volumetric cell segmentation.