8 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…
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…
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…
An Evaluation of DUSt3R/MASt3R/VGGT 3D Reconstruction on Photogrammetric Aerial Blocks
Xinyi Wu, Steven Landgraf, Markus Ulrich +1
State-of-the-art 3D computer vision algorithms continue to advance in handling sparse, unordered image sets. Recently developed foundational models for 3D reconstruction, such as D…
Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustness
Steven Landgraf, Markus Hillemann, Markus Ulrich
Semantic segmentation is critical for scene understanding but demands costly pixel-wise annotations, attracting increasing attention to semi-supervised approaches to leverage abund…