60 citations · 61 across the 7 of their papers we have counts for
6 papers · 1 filter
Out-of-Distribution Object Detection in Street Scenes via Synthetic Outlier Exposure and Transfer Learning
Sadia Ilyas, Annika Mütze, Klaus Friedrichs +2
Out-of-distribution (OOD) object detection is an important yet underexplored task. A reliable object detector should be able to handle OOD objects by localizing and correctly class…
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…
Background-Foreground Segmentation for Interior Sensing in Automotive Industry
Claudia Drygala, Matthias Rottmann, Hanno Gottschalk +2
To ensure safety in automated driving, the correct perception of the situation inside the car is as important as its environment. Thus, seat occupancy detection and classification…
PrognoseNet: A Generative Probabilistic Framework for Multimodal Position Prediction given Context Information
Thomas Kurbiel, Akash Sachdeva, Kun Zhao +1
The ability to predict multiple possible future positions of the ego-vehicle given the surrounding context while also estimating their probabilities is key to safe autonomous drivi…
RetinotopicNet: An Iterative Attention Mechanism Using Local Descriptors with Global Context
Thomas Kurbiel, Shahrzad Khaleghian
Convolutional Neural Networks (CNNs) were the driving force behind many advancements in Computer Vision research in recent years. This progress has spawned many practical applicati…