10 papers
The Impact of CutMix on Reliability and Robustness in Semantic Segmentation
Steven Landgraf, Markus Ulrich
Ensuring not only high accuracy but also reliable and robust predictions is critical for the deployment of semantic segmentation models in safety-critical applications such as auto…
A Critical Synthesis of Uncertainty Quantification and Foundation Models for Semantic Segmentation
Steven Landgraf, Joceline Hinz, Markus Ulrich
Foundation models are increasingly breaking what seemed to be impossible not long ago by enabling unprecedented accuracy and cross-domain generalization. Yet their lack of interpre…
MVM-IOD: An Industrial Object-Centric Benchmark Dataset for the Evaluation of 3D Reconstruction Methods
Robert Langendörfer, Markus Hillemann, Markus Ulrich
3D object reconstruction, and camera pose estimation in industrial applications are challenging tasks, as errors are costly while the computation time is often limited. The complex…
Uncertainty Quality of VGGT: An Analysis on the DTU Benchmark Dataset
Markus Hillemann, Robert Langendörfer, Steven Landgraf +1
Visual Geometry Grounded Transformer (VGGT) has already attracted a great deal of attention in a short period of time, not least due to the Best Paper Award at CVPR-2025. Similar t…
MooMIns -- Monocular 3D Reconstruction and Object Pose Estimation from Multiple Instances
Robert Langendörfer, Markus Hillemann, Markus Ulrich
Simultaneous 3D reconstruction and 6D object pose estimation from a single monocular image is an inherently ill-posed problem. In industrial settings, however, multiple instances o…
Beyond Model Size: Probing the Gaps in Visual in-Context Learning by Training a Tiny Model
Sunil Khatri, Steven Landgraf, Markus Ulrich +1
Visual in-Context Learning (VICL) aims at making progress towards adaptive vision models, that can -- based on a few examples -- adapt to a new task at test-time. With the history…