4 citations · 4 across the 4 of their papers we have counts for
4 papers
Rectify, Don't Regret: On-Policy Closed-Loop Training for Multimodal Trajectory Prediction
Harsh Yadav, Christian Bohn, Tobias Meisen
Current trajectory prediction models are primarily trained in an open-loop manner, which often leads to covariate shift and compounding errors when deployed in real-world, closed-l…
Faster Training, Fewer Labels: Self-Supervised Pretraining for Fine-Grained BEV Segmentation
Daniel Busch, Christian Bohn, Thomas Kurbiel +3
Dense Bird's Eye View (BEV) semantic maps are central to autonomous driving, yet current multi-camera methods depend on costly, inconsistently annotated BEV ground truth. We addres…
Efficient Inter-Task Attention for Multitask Transformer Models
Christian Bohn, Thomas Kurbiel, Klaus Friedrichs +2
In both Computer Vision and the wider Deep Learning field, the Transformer architecture is well-established as state-of-the-art for many applications. For Multitask Learning, howev…
Task Weighting through Gradient Projection for Multitask Learning
Christian Bohn, Ido Freeman, Hasan Tercan +1
In multitask learning, conflicts between task gradients are a frequent issue degrading a model's training performance. This is commonly addressed by using the Gradient Projection a…