Publications (5)
Examining Common Paradigms in Multi-Task Learning
Cathrin Elich, Lukas Kirchdorfer, Jan M. Köhler +1
While multi-task learning (MTL) has gained significant attention in recent years, its underlying mechanisms remain poorly understood. Recent methods did not yield consistent perfor…
Weakly Supervised Learning of Multi-Object 3D Scene Decompositions Using Deep Shape Priors
Cathrin Elich, Martin R. Oswald, Marc Pollefeys +1
Representing scenes at the granularity of objects is a prerequisite for scene understanding and decision making. We propose PriSMONet, a novel approach based on Prior Shape knowled…
Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning
Lukas Kirchdorfer, Cathrin Elich, Simon Kutsche +3
With the rise of neural networks in various domains, multi-task learning (MTL) gained significant relevance. A key challenge in MTL is balancing individual task losses during neura…
Learning-based Relational Object Matching Across Views
Cathrin Elich, Iro Armeni, Martin R. Oswald +2
Intelligent robots require object-level scene understanding to reason about possible tasks and interactions with the environment. Moreover, many perception tasks such as scene reco…
3D-BEVIS: Bird's-Eye-View Instance Segmentation
Cathrin Elich, Francis Engelmann, Theodora Kontogianni +1
Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of obj…