6 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…
CRONOS: Benchmarking Counterfactual Physical Consistency in Video Models
León Begiristain, Olaf Dünkel, Adam Kortylewski
Video prediction is increasingly viewed as a path toward generalizable world models, yet it remains unclear whether these systems learn underlying causal structure or merely exploi…
Attention (as Discrete-Time Markov) Chains
Yotam Erel, Olaf Dünkel, Rishabh Dabral +3
We introduce a new interpretation of the attention matrix as a discrete-time Markov chain. Our interpretation sheds light on common operations involving attention scores such as se…
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…
Do It Yourself: Learning Semantic Correspondence from Pseudo-Labels
Olaf Dünkel, Thomas Wimmer, Christian Theobalt +2
Finding correspondences between semantically similar points across images and object instances is one of the everlasting challenges in computer vision. While large pre-trained visi…
Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Space
Leonhard Sommer, Olaf Dünkel, Christian Theobalt +1
3D morphable models (3DMMs) are a powerful tool to represent the possible shapes and appearances of an object category. Given a single test image, 3DMMs can be used to solve variou…