4 papers
Geometry Matters: 3D Foundation Priors for Learning Semantic Correspondence
Artur Jesslen, Olaf Dünkel, Adam Kortylewski
Foundation features from self-supervised vision models and text-to-image diffusion models have proven effective for semantic correspondence estimation. However, because these featu…
Category-Level 3D Correspondence in Camera Space via Morphable Object Priors
Leonhard Sommer, Artur Jesslen, Basavaraj Sunagad +1
Understanding 3D objects from images is fundamental to robotics and AR/VR applications. While recent work has made progress in category-level pose estimation, current representatio…
Interpretable 3D Neural Object Volumes for Robust Conceptual Reasoning
Nhi Pham, Artur Jesslen, Bernt Schiele +2
With the rise of deep neural networks, especially in safety-critical applications, robustness and interpretability are crucial to ensure their trustworthiness. Recent advances in 3…
CNS-Bench: Benchmarking Image Classifier Robustness Under Continuous Nuisance Shifts
Olaf Dünkel, Artur Jesslen, Jiahao Xie +3
An important challenge when using computer vision models in the real world is to evaluate their performance in potential out-of-distribution (OOD) scenarios. While simple synthetic…