1 citations · 1 across the 5 of their papers we have counts for
6 papers · 1 filter
OSSA: Unsupervised One-Shot Style Adaptation
Robin Gerster, Holger Caesar, Matthias Rapp +2
Despite their success in various vision tasks, deep neural network architectures often underperform in out-of-distribution scenarios due to the difference between training and targ…
OpenPSG: Open-set Panoptic Scene Graph Generation via Large Multimodal Models
Zijian Zhou, Zheng Zhu, Holger Caesar +1
Panoptic Scene Graph Generation (PSG) aims to segment objects and recognize their relations, enabling the structured understanding of an image. Previous methods focus on predicting…
ICP-Flow: LiDAR Scene Flow Estimation with ICP
Yancong Lin, Holger Caesar
Scene flow characterizes the 3D motion between two LiDAR scans captured by an autonomous vehicle at nearby timesteps. Prevalent methods consider scene flow as point-wise unconstrai…
Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving
Napat Karnchanachari, Dimitris Geromichalos, Kok Seang Tan +8
Machine Learning (ML) has replaced traditional handcrafted methods for perception and prediction in autonomous vehicles. Yet for the equally important planning task, the adoption o…
Graph Convolutional Networks for Complex Traffic Scenario Classification
Tobias Hoek, Holger Caesar, Andreas Falkovén +1
A scenario-based testing approach can reduce the time required to obtain statistically significant evidence of the safety of Automated Driving Systems (ADS). Identifying these scen…
Region-based semantic segmentation with end-to-end training
Holger Caesar, Jasper Uijlings, Vittorio Ferrari
We propose a novel method for semantic segmentation, the task of labeling each pixel in an image with a semantic class. Our method combines the advantages of the two main competing…